Research projects funded by the National Institutes of Health (NIH), other DHHS Operating Divisions (ACF, AHRQ, CDC, FDA, HRSA), and the Department of Veterans Affairs. The ExPORTER files provide weekly and/or yearly snapshots of the data publicly accessible through the NIH Research Portfolio Online Reporting Tools, Expenditures and Results (RePORTER) system at https://reporter.nih.gov. The RePORTER database can also be queried using the user interface or the API. The RePORTER database contains information such as project title, abstract, principal investigator, funded organization, total awarded costs, categorization by area of research (NIH only), and project keywords. Also available is information on research publications and patents that have cited support from each project.

A database of federally funded biomedical research projects conducted at universities, hospitals, and other research institutions that provides a central point of access to reports, data, and analyses of NIH research. The RePORTER has replaced the CRISP database. The database, maintained by the Office of Extramural Research at the National Institutes of Health, includes projects funded by the National Institutes of Health (NIH), Substance Abuse and Mental Health Services (SAMHSA), Health Resources and Services Administration (HRSA), Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDCP), Agency for Health Care Research and Quality (AHRQ), and Office of Assistant Secretary of Health (OASH).

The List of Serials Indexed for Online Users (LSIOU) provides bibliographic information for all journals whose articles were ever indexed over time with the MeSH® vocabulary and cited in MEDLINE®, the backbone of the NLM PubMed® database. It includes titles that ceased publication, changed titles, or are no longer indexed. More detailed bibliographic data and information about indexing coverage for serials cited in MEDLINE/PubMed can be found in LocatorPlus Catalog®, the NLM online catalog, and the NLM Catalog, an Entrez database.

The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems.

Patient data from Orthoload online database and gait analysis.

TOXLINE was the National Library of Medicine (NLM) bibliographic database for toxicology, a varied science encompassing many disciplines. TOXLINE records provide bibliographic information covering the biochemical, pharmacological, physiological, and toxicological effects of drugs and other chemicals. TOXLINE references were drawn from various sources organized into component subfiles.

This version of TOXLINE is no longer updated. Updated TOXLINE content is available in PubMed or by searching PubMed using the search string: tox [sb] .

The Dietary Supplement Ingredient Database (DSID) provides estimated levels of ingredients in dietary supplement products sold in the United States. These statistically predicted estimates may differ from labeled amounts and are based on chemical analysis of nationally representative products. The DSID was developed by the Nutrient Data Laboratory, US Department of Agriculture, in collaboration with the Office of Dietary Supplements at the National Institutes of Health (NIH) and other federal agencies. DSID-4 reports national estimates of ingredient content in adult, children’s and non-prescription prenatal multivitamin/mineral (MVMs) and omega-3 fatty acid supplements. New! Analytically-validated mean estimates for vitamin D, vitamin A and chromium in adult MVMs are reported for the first time, and estimates for 18 other ingredients have been calculated based on a new, second study of representative adult MVMs. DSID-4 also reports results for the first DSID study of botanical dietary supplements. The “Green Tea Research Summary and Results" are available on the 'Botanicals' page. The DSID is intended primarily for research applications. These data are appropriate for use in population studies of nutrient intake rather than for assessing content of individual products. Resources in this dataset:Resource Title: The Dietary Supplement Ingredient Database (DSID), Release 4. File Name: Web Page, url: https://dietarysupplementdatabase.usda.nih.gov/ provides estimated levels of ingredients in dietary supplement products sold in the United States. These statistically predicted estimates may differ from labeled amounts and are based on chemical analysis of nationally representative products. The DSID was developed by the Nutrient Data Laboratory, US Department of Agriculture, in collaboration with the Office of Dietary Supplements at the National Institutes of Health (NIH) and other federal agencies. DSID-4 reports national estimates of ingredient content in adult, children’s and non-prescription prenatal multivitamin/mineral (MVMs) and omega-3 fatty acid supplements.

References retrieved through online international databases.

LinkOut is a service that allows you to link directly from PubMed and other NCBI databases to a wide range of information and services beyond the NCBI systems. LinkOut aims to facilitate access to relevant online resources in order to extend, clarify, and supplement information found in NCBI databases.

Third parties can link directly from PubMed and other Entrez database records to relevant Web-accessible resources beyond the Entrez system. Includes full-text publications, biological databases, consumer health information and research tools.

Background HealthCyberMap aims at mapping parts of health information cyberspace in novel ways to deliver a semantically superior user experience. This is achieved through "intelligent" categorisation and interactive hypermedia visualisation of health resources using metadata, clinical codes and GIS. HealthCyberMap is an ArcView 3.1 project. WebView, the Internet extension to ArcView, publishes HealthCyberMap ArcView Views as Web client-side imagemaps. The basic WebView set-up does not support any GIS database connection, and published Web maps become disconnected from the original project. A dedicated Internet map server would be the best way to serve HealthCyberMap database-driven interactive Web maps, but is an expensive and complex solution to acquire, run and maintain. This paper describes HealthCyberMap simple, low-cost method for "patching" WebView to serve hypermaps with dynamic database drill-down functionality on the Web.

   Results
   The proposed solution is currently used for publishing HealthCyberMap GIS-generated navigational information maps on the Web while maintaining their links with the underlying resource metadata base.


   Conclusion
   The authors believe their map serving approach as adopted in HealthCyberMap has been very successful, especially in cases when only map attribute data change without a corresponding effect on map appearance. It should be also possible to use the same solution to publish other interactive GIS-driven maps on the Web, e.g., maps of real world health problems.

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a national genetics data repository facilitating access to genotypic and phenotypic data for Alzheimer's disease (AD). Data include GWAS, whole genome (WGS) and whole exome (WES), expression, RNA Seq, and CHIP Seq analyses. Data for the Alzheimer’s Disease Sequencing Project (ADSP) are available through a partnership with dbGaP (ADSP at dbGaP). Results are integrated and annotated in the searchable genomics database that also provides access to a variety of software packages, analytic pipelines, online resources, and web-based tools to facilitate analysis and interpretation of large-scale genomic data. Data are available as defined by the NIA Genomics of Alzheimer’s Disease Sharing Policy and the NIH Genomics Data Sharing Policy. Investigators return secondary analysis data to the database in keeping with the NIAGADS Data Distribution Agreement.

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...

Self-citation analysis data based on PubMed Central subset (2002-2005) ---------------------------------------------------------------------- Created by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik on April 5th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Fegley BD, Diesner J, Torvik VI (2018) Self-Citation is the Hallmark of Productive Authors, of Any Gender. PLOS ONE. It contains files for running the self citation analysis on articles published in PubMed Central between 2002 and 2005, collected in 2015. The dataset is distributed in the form of the following tab separated text files: * Training_data_2002_2005_pmc_pair_First.txt (1.2G) - Data for first authors * Training_data_2002_2005_pmc_pair_Last.txt (1.2G) - Data for last authors * Training_data_2002_2005_pmc_pair_Middle_2nd.txt (964M) - Data for middle 2nd authors * Training_data_2002_2005_pmc_pair_txt.header.txt - Header for the data * COLUMNS_DESC.txt file - Descriptions of all columns * model_text_files.tar.gz - Text files containing model coefficients and scores for model selection. * results_all_model.tar.gz - Model coefficient and result files in numpy format used for plotting purposes. v4.reviewer contains models for analysis done after reviewer comments. * README.txt file ## Dataset creation Our experiments relied on data from multiple sources including properitery data from Thompson Rueter's (now Clarivate Analytics) Web of Science collection of MEDLINE citations. Author's interested in reproducing our experiments should personally request from Clarivate Analytics for this data. However, we do make a similar but open dataset based on citations from PubMed Central which can be utilized to get similar results to those reported in our analysis. Furthermore, we have also freely shared our datasets which can be used along with the citation datasets from Clarivate Analytics, to re-create the datased used in our experiments. These datasets are listed below. If you wish to use any of those datasets please make sure you cite both the dataset as well as the paper introducing the dataset. * MEDLINE 2015 baseline: https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html * Citation data from PubMed Central (original paper includes additional citations from Web of Science) * Author-ity 2009 dataset: - Dataset citation: Torvik, Vetle I.; Smalheiser, Neil R. (2018): Author-ity 2009 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4222651_V1 - Paper citation: Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3), 1–29. https://doi.org/10.1145/1552303.1552304 - Paper citation: Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2004). A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140–158. https://doi.org/10.1002/asi.20105 * Genni 2.0 + Ethnea for identifying author gender and ethnicity: - Dataset citation: Torvik, Vetle (2018): Genni + Ethnea for the Author-ity 2009 dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9087546_V1 - Paper citation: Smith, B. N., Singh, M., & Torvik, V. I. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL ’13. ACM Press. https://doi.org/10.1145/2467696.2467720 - Paper citation: Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington DC, USA. http://hdl.handle.net/2142/88927 * MapAffil for identifying article country of affiliation: - Dataset citation: Torvik, Vetle I. (2018): MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4354331_V1 - Paper citation: Torvik VI. MapAffil: A Bibliographic Tool for Mapping Author Affiliation Strings to Cities and Their Geocodes Worldwide. D-Lib magazine : the magazine of the Digital Library Forum. 2015;21(11-12):10.1045/november2015-torvik * IMPLICIT journal similarity: - Dataset citation: Torvik, Vetle (2018): Author-implicit journal, MeSH, title-word, and affiliation-word pairs based on Author-ity 2009. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4742014_V1 * Novelty dataset for identify article level novelty: - Dataset citation: Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1 - Paper citation: Mishra S, Torvik VI. Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib magazine : The Magazine of the Digital Library Forum. 2016;22(9-10):10.1045/september2016-mishra - Code: https://github.com/napsternxg/Novelty * Expertise dataset for identifying author expertise on articles: * Source code provided at: https://github.com/napsternxg/PubMed_SelfCitationAnalysis Note: The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check here for information to get PubMed/MEDLINE, and NLMs data Terms and Conditions Additional data related updates can be found at Torvik Research Group ## Acknowledgments This work was made possible in part with funding to VIT from NIH grant P01AG039347 and NSF grant 1348742. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## License Self-citation analysis data based on PubMed Central subset (2002-2005) by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik is licensed under a Creative Commons Attribution 4.0 International License. Permissions beyond the scope of this license may be available at https://github.com/napsternxg/PubMed_SelfCitationAnalysis.

Original records retrieved from Pubmed, SCOPUS, MEDLINE-Ovid, EMBASE, Web of Science and Cochrane Central Register of Controlled Trials databases.

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control. Resources in this dataset:Resource Title: Website Pointer for Plant Expression Database, Iowa State University. File Name: Web Page, url: https://www.bcb.iastate.edu/plant-expression-database [NOTE: PLEXdb is no longer available online. Oct 2019.] Project description for the Plant Expression Database (PLEXdb) and integrated tools.

Carcinogenic Potency Database (CPDB) is a single standardized resource of the results of 45 years of chronic, long-term carcinogenesis bioassays. The experiments vary widely in design, histopathological examination and nomenclature, and in the published authors’ choices of what information to publish in their papers.

Data are included from 6153 experiments reported in the general literature and in the in Technical Reports of the National Cancer Institute/National Toxicology Program (NCI/NTP). Information is given in the CPDB on strain, sex, route of compound administration, target organ, histopathology, author’s opinion about carcinogenicity, and reference to the published paper, as well as quantitative data on statistical significance, tumor incidence, dose-response curve shape, length of experiment, duration of dosing, and dose-rate.

The files on this Web site for the Excel format include (A) documentation of methods, field descriptions, and linking instructions; (B) Excel files; and (C) ancillary files of appendices. NOTE: This dataset is no-longer updated with new content.

The Lesser Sunda Islands in eastern Indonesia cover a longitudinal distance of some 600 kilometres. They are the westernmost place where languages of the Austronesian family come into contact with a family of Papuan languages and constitute an area of high linguistic diversity. Despite its diversity, the Lesser Sundas are little studied and for most of the region, written historical records, as well as archaeological and ethnographic data are lacking. In such circumstances the study of relationships between languages through their lexicon is a unique tool for making inferences about human (pre-)history and tracing population movements. However, the lack of a collective body of lexical data has severely limited our understanding of the history of the languages and peoples in the Lesser Sundas. The LexiRumah database fills this gap by assembling lexicons of Lesser Sunda languages from published and unpublished sources, and making those lexicons available online in a consistent format. This database makes it possible for researchers to explore the linguistic data collated from different primary sources, to formulate hypotheses on how the languages of the two families might be internally related and to compare competing hypotheses about subgroupings and language contact in the region. In this article, we present observations from aggregating lexical data from sources of different type and quality, including fieldwork, and generalize our lessons learned towards practical guidelines for creating a consistent database of comparable lexical items, derived from the design and development of LexiRumah. Databases like this are instrumental in developing theories of language evolution and change in understudied regions where small-scale, pre-industrial, pre-literate societies are the majority. It is therefore vital to follow reliable design choices when creating such databases, as described in this paper.

Table listing all potential UTRs identified through available online databases.

The Tuberculosis (TB) Chest X-ray Database is a collaborative effort between researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh, and their counterparts from Malaysia, in association with medical practitioners from Hamad Medical Corporation and Bangladesh. The database consists of chest X-ray images of both normal individuals (3500) and TB positive cases (700 publicly accessible images and 2800 images that can be obtained from the NIAID TB portal[3] by signing a data-sharing agreement).

Contribution

• This dataset contains CXR images of Normal (3500) and patients with TB (700 TB images in publicly accessible and 2800 TB images can be downloaded from NIAID TB portal[3] by signing an agreement). The TB database is collected from the source:

NLM dataset: National Library of Medicine (NLM) in the U.S. [1] has made two lung X-ray datasets publicly available: the Montgomery and Shenzhen datasets.

Belarus dataset: Belarus Set [2] was collected for a drug resistance study initiated by the National Institute of Allergy and Infectious Diseases, Ministry of Health, Republic of Belarus.

NIAID TB dataset: NIAID TB portal program dataset [3], which contains about 3000 TB positive CXR images from about 3087 cases. Weblink: https://tbportals.niaid.nih.gov/download-data

RSNA CXR dataset: RSNA pneumonia detection challenge dataset [4], which is comprised of about 30,000 chest X-ray images, where 10,000 images are normal and others are abnormal and lung opacity images.

Objective

Researchers can use this database to produce useful and impactful scholarly work on TB, which can help in tackling this issue.

Citation

• Please cite this database if you are using it for any scientific purpose: Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020) "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". IEEE Access, Vol. 8, pp 191586 - 191601. DOI. 10.1109/ACCESS.2020.3031384.

References:

[1] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, "Two public chest X-ray datasets for computer-aided screening of pulmonary diseases," Quantitative imaging in medicine and surgery, vol. 4 (6), p. 475(2014) [2] B. P. Health. (2020). BELARUS TUBERCULOSIS PORTAL [Online]. Available: http://tuberculosis.by/. [Accessed on 09-June-2020] [3] NIAID TB portal program dataset [Online]. Available: https://tbportals.niaid.nih.gov/download-data. [4] kaggle. RSNA Pneumonia Detection Challenge [Online]. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. [Accessed on 09-June-2020]

Glioblastoma is the most common and fatal primary brain tumor in adults. Even with maximal resection and a series of postoperative adjuvant treatments, the median overall survival (OS) of glioblastoma patients remains approximately 15 months. The Huashan Hospital glioma bank contains more than 2000 glioma tissue samples with long-term follow-up data; almost half of these samples are from glioblastoma patients. Several large glioma databases with long-term follow-up data have reported outcomes of glioblastoma patients from countries other than China. We investigated the prognosis of glioblastoma patients in China and compared the survival outcomes among patients from different databases. The data for 967 glioblastoma patients who underwent surgery at Huashan Hospital and had long-term follow-up records were obtained from our glioma registry (diagnosed from 29 March 2010, through 7 June 2017). Patients were eligible for inclusion if they underwent surgical resection for newly diagnosed glioblastomas and had available data of survival and personal information. Data of 778 glioblastoma patients were collected from three separate online databases (448 patients from The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov), 191 from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) database (GSE108476) and 132 from data set GSE16011(Hereafter called as the French database). We compared the prognosis of glioblastoma patients from records among the different databases and the changes in survival outcomes of glioblastoma patients from Huashan Hospital over an 8-year period. The median OS of glioblastoma patients was 16.3 (95% CI: 15.4–17.2) months for Huashan Hospital, 13.8 (95% CI: 12.9–14.9) months for TCGA, 19.3 (95% CI: 17.0–20.0) months for the REMBRANDT database, and 9.1 months for the French database. The median OS of glioblastoma patients from Huashan Hospital improved from 15.6 (2010–2013, 95% CI: 14.4–16.6) months to 18.2 (2014–2017, 95% CI: 15.8–20.6) months over the study period (2010–2017). In addition, the prognosis of glioblastoma patients with total resection was significantly better than that of glioblastoma patients with sub-total resection or biopsy. Our study confirms that treatment centered around maximal surgical resection brought survival benefits to glioblastoma patients after adjusting to validated prognostic factors. In addition, an improvement in prognosis was observed among glioblastoma patients from Huashan Hospital over the course of our study. We attributed it to the adoption of a new standard of neurosurgical treatment on the basis of neurosurgical multimodal technologies. Even though the prognosis of glioblastoma patients remains poor, gradual progress is being made.