Notes:– All examples originated from the literatures regarding portal vein thrombosis.– In every example, the same study was simultaneously recorded by two or three databases.– All literatures were expressed in Vancouver reference type.– Bold and italics formatting indicated the different styles between index and redundant paper(s).– In every example, the reference recorded by PubMed database had more complete information.

The purest type of electronic clinical data which is obtained at the point of care at a medical facility, hospital, clinic or practice. Often referred to as the electronic medical record (EMR), the EMR is generally not available to outside researchers. The data collected includes administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, patient insurance, etc.

Individual organizations such as hospitals or health systems may provide access to internal staff. Larger collaborations, such as the NIH Collaboratory Distributed Research Network provides mediated or collaborative access to clinical data repositories by eligible researchers. Additionally, the UW De-identified Clinical Data Repository (DCDR) and the Stanford Center for Clinical Informatics allow for initial cohort identification.

About Dataset:

333 scholarly articles cite this dataset.

Unique identifier: DOI

Dataset updated: 2023

Authors: Haoyang Mi

In this dataset, we have two dataset:

1- Clinical Data_Discovery_Cohort: Name of columns: Patient ID Specimen date Dead or Alive Date of Death Date of last Follow Sex Race Stage Event Time

2- Clinical_Data_Validation_Cohort Name of columns: Patient ID Survival time (days) Event Tumor size Grade Stage Age Sex Cigarette Pack per year Type Adjuvant Batch EGFR KRAS

Feel free to put your thought and analysis in a notebook for this datasets. And you can create some interesting and valuable ML projects for this case. Thanks for your attention.

Background Wikidata is a community and database within the Wikipedia ecosystem.

WikiProject Clinical Trials is a community project in Wikidata to curate data related to clinical trials for its use in the Wikipedia ecosystem or export to elsewhere. Visit the project at https://www.wikidata.org/wiki/Wikidata:WikiProject_Clinical_Trials

About this record

The files in this record are a snapshot of the content output and appearance of WikiProject Clinical Trials in February 2022. The Jupyter Notebook (1 WikiProject Clinical Trials 2022-02.ipynb) contains example SPARQL queries which call Wikidata content, and the data files are the present results from those queries. As anyone can edit Wikidata and its content grows with time, query results will change over time. The queries are as follows:

1 Model profiles

1.1 Clinical trials for Zika fever

1.2 Clinical trials using COVID-19 vaccine

1.3 Clinical trials at Vanderbilt University

1.4 Clinical trials with Julie McElrath as principal investigator

1.5 Clinical trials funded by Patient-Centered Outcomes Research Institute

2 Topics by count of clinical trials

2.1 Medical conditions

2.2 Research interventions

2.3 Research sites

2.4 Principal investigators

2.5 Funders

3 Organizational affiliations

3.1 Clinical trials with principal investigator and their affiliation

3.2 Clinical trials where principal investigator has Vanderbilt University affiliation

3.3 Chart of organizations by count of clinical trials

3.4 Clinical trials where the sponsor was Pfizer

4 Researcher demographics

4.1 Count of principal investigators by gender

4.2 Clinical trials where the principal investigator is female

4.3 Principal investigators by occupation

5 Scope of Wikidata's clinical trials content

5.1 List of clinical trials

5.2 Count of clinical trials

5.3 Most common properties applied to clinical trials

5.4 Count of statements in clinical trial records

5.5 Count of trial records in Wikidata per clinical trial registry

Also included in this record are screenshots of WikiProject Clinical Trials as it looks now.

Online access

Again, the project at Wikidata is at https://www.wikidata.org/wiki/Wikidata:WikiProject_Clinical_Trials . The Wikidata Query Service accessible through the "Query" page there assists users in modifying these or any queries to search for different targets, such as other medical conditions or institutions of interest. Another way to access the content online is by accessing the notebook through a copy in GitHub, such as at https://github.com/bluerasberry/WikiProject-Clinical-Trials and rendering it through an online viewer, such as https://mybinder.org .

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

Abstract MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1]. The MIMIC-III Clinical Database is available on PhysioNet (doi: 10.13026/C2XW26). Though deidentified, MIMIC-III contains detailed information regarding the care of real patients, and as such requires credentialing before access. To allow researchers to ascertain whether the database is suitable for their work, we have manually curated a demo subset, which contains information for 100 patients also present in the MIMIC-III Clinical Database. Notably, the demo dataset does not include free-text notes.

Background In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. Despite this advance, interoperability of digital systems remains an open issue, leading to challenges in data integration. As a result, the potential that hospital data offers in terms of understanding and improving care is yet to be fully realized.

MIMIC-III integrates deidentified, comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, and makes it widely accessible to researchers internationally under a data use agreement. The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible.

The MIMIC-III database was populated with data that had been acquired during routine hospital care, so there was no associated burden on caregivers and no interference with their workflow. For more information on the collection of the data, see the MIMIC-III Clinical Database page.

Methods The demo dataset contains all intensive care unit (ICU) stays for 100 patients. These patients were selected randomly from the subset of patients in the dataset who eventually die. Consequently, all patients will have a date of death (DOD). However, patients do not necessarily die during an individual hospital admission or ICU stay.

This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified.

Data Description MIMIC-III is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-III Clinical Database page. The demo shares an identical schema, except all rows in the NOTEEVENTS table have been removed.

The data files are distributed in comma separated value (CSV) format following the RFC 4180 standard. Notably, string fields which contain commas, newlines, and/or double quotes are encapsulated by double quotes ("). Actual double quotes in the data are escaped using an additional double quote. For example, the string she said "the patient was notified at 6pm" would be stored in the CSV as "she said ""the patient was notified at 6pm""". More detail is provided on the RFC 4180 description page: https://tools.ietf.org/html/rfc4180

Usage Notes The MIMIC-III demo provides researchers with an opportunity to review the structure and content of MIMIC-III before deciding whether or not to carry out an analysis on the full dataset.

CSV files can be opened natively using any text editor or spreadsheet program. However, some tables are large, and it may be preferable to navigate the data stored in a relational database. One alternative is to create an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

DB Browser for SQLite is a high quality, visual, open source tool to create, design, and edit database files compatible with SQLite. We have found this tool to be useful for navigating SQLite files. Information regarding installation of the software and creation of the database can be found online: https://sqlitebrowser.org/

Release Notes Release notes for the demo follow the release notes for the MIMIC-III database.

Acknowledgements This research and development was supported by grants NIH-R01-EB017205, NIH-R01-EB001659, and NIH-R01-GM104987 from the National Institutes of Health. The authors would also like to thank Philips Healthcare and staff at the Beth Israel Deaconess Medical Center, Boston, for supporting database development, and Ken Pierce for providing ongoing support for the MIMIC research community.

Conflicts of Interest The authors declare no competing financial interests.

References Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Mo...

Database of federally funded research projects pertaining to dietary supplements. CARDS contains projects funded by the United States Department of Agriculture (USDA), the Department of Defense (DOD) and the Institutes and Centers (ICs) of the National Institutes of Health (NIH) beginning with fiscal year 1999, the first year that NIH ICs began reporting research related to dietary supplements. Projects funded by other Federal agencies will be added to CARDS as they become available. The Office of Dietary Supplements (ODS) will post notices on its website and listserv when CARDS updates are completed. Codes assigned to each research project allow the CARDS user to identify: * research related to specific dietary supplement ingredients; for example, vitamin E or St. John''''s wort * the type of study; for example, a Phase III study or an animal study * health outcomes or biological effects; for example, osteoporosis or antioxidant function * whether the research is directly related or indirectly related to dietary supplements. For example, a clinical trial comparing bone density in women given a daily calcium supplement versus a placebo would be classified as directly related to dietary supplements. A study examining the activation of steroid hormone receptors by supplemental vitamin D in cell culture would be classified as indirectly related to dietary supplements because the direct physiological or health effects of vitamin D supplementation are not being studied. A search of the CARDS database can be used to sort and tabulate information for a variety of purposes. For example, a researcher may want to know which ICs at the NIH fund research on herbal supplement ingredients. A consumer may want to know if the Federal government is supporting research on a popular dietary supplement ingredient such as vitamin C.

The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

During the webinar, senior analyst from CIHI presented the Discharge Abstract Database (DAD) Research Analytic Files. This database captures administrative, clinical and demographic information on hospital discharges, including deaths, sign-outs and transfers. There are two files in the DLI that relate to the Discharge Abstract Database. The files are de-identified samples containing record-level data from fiscal years 2009-2010 and 2010-2011. One file contains clinical data and the other geographic data. Both files are available in English and French. In particular, this webinar will focus on using the documentation provided, as well as a few illustrative examples on how to best use the DAD Research Analytic Files.

The largest all-payer ambulatory surgery database in the United States, the Healthcare Cost and Utilization Project (HCUP) Nationwide Ambulatory Surgery Sample (NASS) produces national estimates of major ambulatory surgery encounters in hospital-owned facilities. Major ambulatory surgeries are defined as selected major therapeutic procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain (i.e., surgeries flagged as "narrow" in the HCUP Surgery Flag Software). Unweighted, the NASS contains approximately 9.0 million ambulatory surgery encounters each year and approximately 11.8 million ambulatory surgery procedures. Weighted, it estimates approximately 11.9 million ambulatory surgery encounters and 15.7 million ambulatory surgery procedures. Sampled from the HCUP State Ambulatory Surgery and Services Databases (SASD) and State Emergency Department Databases (SEDD) in order to capture both planned and emergent major ambulatory surgeries, the NASS can be used to examine selected ambulatory surgery utilization patterns. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NASS contains clinical and resource-use information that is included in a typical hospital-owned facility record, including patient characteristics, clinical diagnostic and surgical procedure codes, disposition of patients, total charges, facility characteristics, and expected source of payment, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NASS excludes data elements that could directly or indirectly identify individuals, hospitals, or states. The NASS is limited to encounters with at least one in-scope major ambulatory surgery on the record, performed at hospital-owned facilities. Procedures intended primarily for diagnostic purposes are not considered in-scope. Restricted access data files are available with a data use agreement and brief online security training.

The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalography (EEG) recordings from 1,020 comatose patients with a diagnosis of cardiac arrest who were admitted to an intensive care unit from seven academic hospitals in the U.S. and Europe. Patients were monitored with 18 bipolar EEG channels over hours to days for the diagnosis of seizures and for neurological prognostication. Long-term neurological function was determined using the Cerebral Performance Category scale.

In introductory courses, students learn about microtubules as important structures but may not engage in a hands-on experience to localize microtubules themselves or to learn about their connection to cancer treatment. In this lesson, students review microtubule structure and function and then design a concept map based on what they have learned. Students also conduct an immunofluorescence procedure using budding yeast cells to observe microtubule localization at different stages of cell division. This technique involves using alpha-tubulin-specific antibodies which work on both yeast and mammalian cells. In the second part of the lesson, students examine their results from the immunofluorescence procedure using fluorescence microscopy and begin to explore different classes of chemotherapy drugs that alter microtubule structure in eukaryotic cells. They also search a clinical trials database to find examples where these microtubule-altering drugs are used for cancer treatment. Many students may have heard of chemotherapy as part of a first line treatment for cancer but may not understand how certain drugs disrupt microtubules to stop cancer. Students report back what they have learned about the different classes of microtubule drugs in small groups, and then add to their concept maps to introduce where a drug may alter microtubule structure and/or function. Using a combination of on-line tools and in class laboratory work, this lesson strengthens students’ understanding of microtubule structure and function, critical to the life of the cell. Students are assessed for their understanding of the topic in several ways, including as a part of a laboratory exam.

Primary Image: Concept Map and Immunofluorescence Results. Shown here in an example of a concept map (A) of the structure and function of microtubules created by the author based on the best examples of student work as well as a fluorescence microscopy image (B) of the results of a student immunofluorescence procedure to observe microtubules in budding yeast. The microtubules are shown in green.

Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.Scopri di più

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articlesOpen Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation CollectionDefinitions for individual data fields:pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicinedoi: Digital Object Identifier, if availableyear: Year the article was publishedtitle: Title of the articleauthors: List of author namesjournal: Journal name (ISO abbreviation)is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research articlerelative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.citation_count: Number of unique articles that have cited this onecitations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)x_coord: X coordinate of the article on the Triangle of Biomediciney_coord: Y Coordinate of the article on the Triangle of Biomedicineis_clinical: Flag indicating that this paper meets the definition of a clinical article.cited_by_clin: PMIDs of clinical articles that this article has been cited by.apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.cited_by: PMIDs of articles that have cited this one.references: PMIDs of articles in this article's reference list.Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.Comments and questions can be addressed to iCite@mail.nih.gov

Digitization of healthcare data along with algorithmic breakthroughts in AI will have a major impact on healthcare delivery in coming years. Its intresting to see application of AI to assist clinicians during patient treatment in a privacy preserving way. While scientific knowledge can help guide interventions, there remains a key need to quickly cut through the space of decision policies to find effective strategies to support patients during the care process.

Offline Reinforcement learning (also referred to as safe or batch reinforcement learning) is a promising sub-field of RL which provides us with a mechanism for solving real world sequential decision making problems where access to simulator is not available. Here we assume that learn a policy from fixed dataset of trajectories with further interaction with the environment(agent doesn't receive reward or punishment signal from the environment). It has shown that such an approach can leverage vast amount of existing logged data (in the form of previous interactions with the environment) and can outperform supervised learning approaches or heuristic based policies for solving real world - decision making problems. Offline RL algorithms when trained on sufficiently large and diverse offline datasets can produce close to optimal policies(ability to generalize beyond training data).

As Part of my PhD, research, I investigated the problem of developing a Clinical Decision Support System for Sepsis Management using Offline Deep Reinforcement Learning.

MIMIC-III ('Medical Information Mart for Intensive Care') is a large open-access anonymized single-center database which consists of comprehensive clinical data of 61,532 critical care admissions from 2001–2012 collected at a Boston teaching hospital. Dataset consists of 47 features (including demographics, vitals, and lab test results) on a cohort of sepsis patients who meet the sepsis-3 definition criteria.

we try to answer the following question:

Given a particular patient’s characteristics and physiological information at each time step as input, can our DeepRL approach, learn an optimal treatment policy that can prescribe the right intervention(e.g use of ventilator) to the patient each stage of the treatment process, in order to improve the final outcome(e.g patient mortality)?

we can use popular state-of-the-art algorithms such as Deep Q Learning(DQN), Double Deep Q Learning (DDQN), DDQN combined with BNC, Mixed Monte Carlo(MMC) and Persistent Advantage Learning (PAL). Using these methods we can train an RL policy to recommend optimum treatment path for a given patient.

Data acquisition, standard pre-processing and modelling details can be found here in Github repo: https://github.com/asjad99/MIMIC_RL_COACH

Abstract During analysis of scientific research data, it is customary to encounter anomalous values or missing data. Anomalous values can be the result of errors of recording, typing, measurement by instruments, or may be true outliers. This review discusses concepts, examples and methods for identifying and dealing with such contingencies. In the case of missing data, techniques for imputation of the values are discussed in, order to avoid exclusion of the research subject, if it is not possible to retrieve information from registration forms or to re-address the participant.

Examples of database entries and their ratings (AM: Aggregation method).

Examples of levels of standardisation in TBDBT, for patient age, gender, and date of diagnosis.

The primary outcome of this study was to examine discrepancies between the reported primary and secondary outcomes in registered and published randomized controlled trials in high impact-factor obesity journals. The secondary outcomes were to address whether outcome reporting discrepancies favor statistically significant outcomes, whether there was a correlation between funding source and likelihood of outcome reporting bias, and whether there were temporal trends in outcome reporting bias. We also catalogued any incidental findings during data extraction and analysis that warranted further examination. To accomplish these aims, we performed a methodological systematic review of the 4 highest impact-factor obesity journals from 2013-2015. This study did not meet the regulatory definition of human subjects research according to 45 CFR 46.102(d) and (f) of the Department of Health and Human Services’ Code of Federal Regulations 10 and was not subject to Institutional Review Board oversight. We consulted Li, et al.; the Cochrane Handbook for Systematic Reviews of Interventions; and the National Academies of Science, Engineering, and Medicine’s (previously the Institute of Medicine) Standards for Systematic Reviews to ensure best practices regarding data extraction and management. We applied PRISMA guideline items 1, 3, 5-11, 13, 16-18, 24-27 to ensure reporting quality for systematic reviews as well as SAMPL guidelines for reporting descriptive statistics. Prior to initiation of the study, we registered this study with the University hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) with registry number: R000025787UMIN000022576.

After screening, the citations were imported into the Agency for Healthcare Research and Quality’s Systematic Review Data Repository (SRDR) for data extraction.Two investigators (J.R.,A.R.) independently reviewed the full-text articles for each study and extracted data using SRDR. At least once per day, these investigators would trade articles and repeat the other’s data extraction. This allowed each to cross-validate the other’s work and improve the accuracy and efficiency of data extraction. Any disagreements were resolved by discussion between the pair. A third party reviewer (M.V.) was available for further adjudication, but was not needed. We extracted the following items from the published randomized controlled trials: primary outcome(s), secondary outcome(s), date of subject enrollment, trial registry database and registration number, timing of assessment in primary outcomes (ex.change in weight at 5 months, change in HbA1c at 6 weeks), sample size, any discrepancies between publication and registry disclosed by the author in the publication, and funding source. For the purpose of our study we classified funding source into the following categories: (1) private (ex. Mayo Clinic or philanthropic), (2) public (government or university), (3) industry/corporate (ex. GlaxoSmithKline), (4) University Hospital, (5) mixed funding source, or (6) undisclosed funding source. For RCTs which reported multiple primary and secondary outcomes, we recorded each explicitly stated outcome. If a primary outcome was not explicitly stated as such in the publication, the outcome stated in the sample size estimation was used. If none was explicitly stated in the text or in the sample size calculation, the article was excluded from the study. When sample size was not explicitly stated in the article, we used the “number randomized”. If author’s failed to differentiate between primary and secondary outcomes in the publication, these non-delineated outcomes were coded as “unable to assess” and excluded from comparison. The clinical trial registry or registration number was obtained from each published RCT, if stated, during full-text review/data extraction. If a registration number was listed in the RCT without a trial registry, a search was made of Clinicaltrials.gov, the International Standard Randomized Controlled Trial Number Register (ISRCTNR), the World Health Organization’s International Clinical Trial Registry Platform (ICTRP), and any country specific clinical trial registry identified in the publication. The following characteristics were used to match registered study to publication: title, author(s), keyword, country of origin, sponsoring organization, description of study intervention, projected sample size, and dates of enrollment. When a publication did not explicitly state information regarding registration of a study, the authors were contacted via email using a standardized email template and asked about registration status. If after 2 days there was no reply, a 2nd email was sent. If there was no reply from authors 1 week after the 2nd email, the study was considered unregistered and excluded from the study.Each registered study was located within its respective registry and data was extracted individually by 2 independent investigators (J.R., A.R.). Prior to registry data extraction both investigators underwent trial registry training including: training videos on how to perform searches and access the history of changes in clinicaltrials.gov and the WHO trial registry, tutorial video about locating desired content from trial registry entry, access to a list of all WHO approved trial registries, and each had to successfully complete a sample data extraction from an unrelated study registry entry. The following data was extracted using a standardized form on SRDR: date of trial registration, date range of subject enrollment, original primary registered outcome(s), final primary registered outcome(s), date of initial primary outcome registration, secondary registered outcome(s), sample size if listed, and funding source, if disclosed, using previously defined categories. Although registration quality was not the focus of this study, registered trials lacking a clearly stated primary outcome and timing of assessment were excluded from consideration. Studies that were found to be registered after the end of subject enrollment were excluded from the study due to the inability to adequately assess outcome reporting bias.To be approved by the WHO, a trial registry must meet ICMJE criteria, including documentation of when changes are made to that particular study’s registry entries. If an included study employed this feature, we recorded both the primary outcome from time of initial registration as well as the primary outcome listed in the final version in the registry entry. Departing from the methods of previous authors in this field of research, we did not exclude studies in WHO-approved registries that did not time-stamp the date of initial primary outcome registration. Per the International Standards for Clinical Trial Registries section 2.4, WHO-approved registries are required to time-stamp registry-approved changes to any registered trial including data additions, deletions, and revisions. Therefore, if a WHO-approved trial registry did not display a history of changes, we recorded the date the registry application was approved as the date of initial primary outcome registration. Additionally, the listed primary outcome was recorded as both the initial registered and final registered primary outcome. In non-WHO-approved trial registries, if a date of initial primary outcome registration was not listed, this trial was excluded from our study.

Participant characteristics of the pooled data setb'*'.