This database allows you to search the CDRH's database information on medical devices which may have malfunctioned or caused a death or serious injury during the years 1992 through 1996.

Description not specified.........................

The Medical Electronic Device History Record (eDHR) software market is experiencing robust growth, projected to reach $309 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 8.6% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing complexity of medical devices necessitates comprehensive and readily accessible documentation for regulatory compliance (FDA, EU MDR, etc.), driving adoption of eDHR solutions. Furthermore, the shift towards digitalization within the healthcare sector, coupled with the advantages of improved data management, enhanced traceability, and reduced operational costs associated with eDHRs, are significant contributors to market growth. The market segmentation reveals strong demand across all medical device classes (I, II, and III), with SaaS models dominating due to their scalability and cost-effectiveness. North America currently holds a substantial market share, owing to stringent regulatory environments and early adoption of advanced technologies. However, significant growth potential exists in other regions, particularly Asia Pacific, driven by increasing healthcare investments and regulatory harmonization. Competition is intense, with established players like MasterControl and Siemens alongside agile startups innovating in areas like AI-driven data analysis and integration with other medical device lifecycle management systems. The market's trajectory suggests continued growth throughout the forecast period (2025-2033), albeit potentially with some moderation as market saturation increases in mature regions. Future growth hinges on advancements in cloud technologies, AI integration for improved data analysis and risk management, and the broadening adoption of eDHR solutions by smaller medical device manufacturers. Addressing concerns around data security and integration complexities remains crucial for sustained market expansion. The continuous evolution of regulatory frameworks will also influence market dynamics, shaping both opportunities and challenges for eDHR software providers. Strategic partnerships and acquisitions are likely to become prominent strategies for companies seeking to expand their market reach and product offerings.

SUMMARY

DDOD use case to request means on consolidating multiple data sources (MDR, PMA, 510(k), R&L) in order to build a list of all marketed medical devices.

WHAT IS A USE CASE?

A “Use Case” is a request that was made by the user community because there were no available datasets that met their particular needs. If this use case is similar to your needs, we ask that you add your own requirements to the specifications section.

The concept of a use case falls within the Demand-Driven Open Data (DDOD) program and gives you a formalized way to identify what data you need. It’s for anyone in industry, research, media, nonprofits or other government agencies. Each request becomes a DDOD use case, so that it can be prioritized and worked on.

Use Cases also provide a wealth of insights about existing alternative datasets and tips for interpreting and manipulating data for specific purposes.

PURPOSE

The US Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) has multiple data sources for registered medical devices, including Medical Device Reporting (MDR), Premarket Approval (PMA), 510(k), and Establishment Registration and Device Listing (R&L). There are a number of challenges associated with the data, including:

• A single marketed product often has multiple 510(k)s
• A PMA might represent multiple marketed products over the course of the evolution of the device
• 510(k) and PMA downloads only include class II and III devices, omitting class I

The only current way to consolidate the information across these various databases in a reliable manner is via FOIA request. There have already been multiple requests for this data at regular refresh intervals.

VALUE

A single source for all registered medical devices is needed for core services on medical device insights provided to hospitals. In addition, without a single source of data, it is difficult for hospitals to understand the available substitutes for a given device.

USE CASE SPECIFICATIONS & SOLUTION

Information about this use cases is maintained in a wiki: http://hhs.ddod.us/wiki/Use_Case_5:_Consolidated_registry_of_marketed_me...

It serves as a knowledge base.

USE CASE DISCUSSION FORUM

All communications between Data Users, DDOD Administrators and Data Owners are logged as discussions within GitHub issues: https://github.com/demand-driven-open-data/ddod-intake/issues/5

It aims to provide complete transparency into the process and ensure the same message gets to all participants.

CASE STATUS

Closed via openFDA.gov API, which includes medical device registration and listing as of September 2015.

The former Techniques Development Laboratory (TDL) of the U.S. National Weather Service began collecting Manually-Digitized Radar (MDR) data in November 1973 from teletype reports for the eastern two-thirds of the United States. In 1978, TDL began archiving the MDR on a new grid aligned with the output grid of the Limited Fine Mesh (LFM) model, which was the model used for numerical weather prediction at the time. The new MDR archive covered the entire conterminous United States. As with the old MDR data, the new data were archived in Model Output Statistics (MOS) predictand format to support thunderstorm prediction. In addition to the "raw" MDR data, TDL also created a gridded 20-nautical-mile-resolution MDR product.

Database of information on the abilities of drugs to interact with an expanding number of molecular targets. It serves as a data warehouse for published and internally-derived Ki, or affinity, values for a large number of drugs and drug candidates at an expanding number of G-protein coupled receptors, ion channels, transporters and enzymes. The query interface is designed to let you search by any field, or combination of them to refine your search criteria. The flexible user interface also provides for customized data mining. The database is regularly updated. If you know of Ki data you would like to add, you can select Direct Ki Entry at the grey panel. If you would like, however, your own data (published or not) added, Send them a Reference at the grey panel, or send an email to Dr. Bryan Roth or Estela Lopez. Most common targets: 5-HT2A, DOPAMINE D1, DOPAMINE D2, 5-HT2C, 5-HT1A, Cholinergic, muscarinic M1, 5-HT Transporter, HISTAMINE H1, 5-HT2B, OPIOID Mu, 5-HT6, adrenergic Beta2, 5-HT7, OPIATE Delta, adrenergic Alpha1A, OPIOID Kappa, 5-HT3, m-AChR, adrenergic Beta1, adrenergic Alpha2A, 5-HT1, Acetylcholinesterase, AChE, Thromboxane A2, n-AChR, Opiate non-selective, CANNABINOID CB1, HERG, Dopamine, cocaine site, adrenergic Alpha2C, M3, Norepinephrine Uptake, Monoamine Oxidase A, Monoamine Oxidase B, 5-HT4, adrenergic Alpha1, 5-HT1E, B1 BRADYKININ, 5-HT2, 5-HT2C-INI, DOPAMINE D4, ANGIOTENSIN AT1, Neurokinin NK1, HISTAMINE H3, Sigma-1, VIP, Dopamine2-like, metabotropic glutamate 5, 5-HT2c VGI, Carbonic Anhydrase Isozymes, CA I, DOPAMINE D2 Long, adrenergic Alpha2, adrenergic Alpha2B, adrenergic Alpha2D, GABA A alpha1, CANNABINOID CB2, adrenergic Alpha1B, 5-HT5a, Melatonin, HISTAMINE H4, NMDA, 5-HT4a, Glucocorticoid, Interleukin 1-beta, Sodium Channel, Benzodiazepine central, Cholinergic, muscarinic M5, Neuropeptide Y1, GABA A alpha5, Galanin R2, Neurokinin NK3, 5-HT1B, M2, DOPAMINE D3, Angiotensin, Dopamine1-like, Neurokinin NK2, adrenergic Beta, Dopamine D1 high, Dopamine D1A, MAP kinase, ADENOSINE A2a, 5-HT7b, Nitrogen oxide synthase - neuronal, Sigma-2, CDK2, Neurotensin 2, DOPAMINE D2 Short, Multidrug Resistance Transporter MDR 1, GABA A Benzodiazepine, VEGF-R2, OPIATE Mu 2, Angiotensin II AT1, HISTAMINE H2, Angiotensin-converting enzyme, ACE, Sigma, beta-amyloid, ADENOSINE, ADENOSINE A2B, Adrenaline, Neurotensin 1

The Hopkins U.S. System Index (HUSSI) is an information resource for forest entomologists, systematic entomologists, pest management specialists, foresters, and students. It is a collection of notes on thousands of insect and damage specimens from forests or wood products taken mainly in the United States, with some from Canada, Mexico, Central America, South America and other regions. Specimens related to the records are in collections at several USDA Forest Service installations; at the U.S. National Museum, Smithsonian Institution, Washington, DC; and at several universities. The paper-based system, conceptualized by Dr. A.D. Hopkins in 1894 and formally initiated by the USDA in 1902, now contains over 160,000 written records. Some of these records have been digitized as follows. The database includes information on location, date, taxon, insect and plant host association; other searches, measurements, and quantitative data; and other information in tabular or narrative form. The original database file was designed for importing into dBase, Access, FoxBase, RBase, Paradox, and other XBase-type programs. The data dictionary describes information entered in the 16 fields abstracted from the Hopkins U.S. System records. Then you can structure specific queries and reports that show:

Plant hosts Insect hosts Parasites & predators Geographic distribution Collection dates and collectors Location of original written notes Location of insect or damage specimens Resources in this dataset:Resource Title: Data files rezipped October 2015. File Name: allwest2.zipResource Description: The original allwest.exe data package offered by U.S. Forest Service was opened using WinZip 15 (Windows 7) and saved as a zip archive suitable for opening with typical archive utilities on both Windows and Macintosh. Downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml.

Includes:

README.TXT : Instructions from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

TITLPAGE.TXT : Title page.

HUSINTRO.TXT : Background information on the Hopkins U.S. System and the Hopkins U.S. System Index (HUSSI).

HUSSTAT.TXT : Description of HUSSI files at each repository.

HUSREPOS.TXT : List of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.

HUSDTDIC.TXT : Data dictionary for HUSSI records.

DBDESAW2.TXT : Description of ALLWEST2 database.

ALLWEST2.DBF : HUSSI records from all western USDA Forest Service repositories (as of 1986), except PSWNB records from notebooks at the Pacific Southwest Experiment Station, Berkeley, CA. PSWNB records are in a seperate archive.Resource Title: Flat version of the HUSSI database. File Name: ALLWEST2.csvResource Description: The file ALLWEST2.DBF from ALLWEST.EXE was converted to a comma separated values file using LibreOffice 5.0.2.2. This appears to include all 37,198 records with 16 columns as described in the data dictionary. Suitable for use with most applications that can handle CSV input.Resource Title: Original text version of HUSSI data dictionary. File Name: HUSDTDIC.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original list of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.. File Name: HUSREPOS.TXTResource Description: Included in ALLWEST2 archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

Expands all the acronyms of the repositories holding physical cards represented in the database.Resource Title: Original README.TXT from the ALLWEST archive. File Name: README.TXTResource Description: Original README.TXT from the ALLWEST archive. The explanations appear in the zipped archive, and have been used as a basis for this dataset description. Includes obsolete instructions for using self-extracting archive on Windows 95 and Windows 3.x operating systems.Resource Title: Original Database Description from ALLWEST2 archive. File Name: DBDESAW2.TXTResource Description: Included in ALLWEST2 archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml. Title: Original introductory text from ALLWEST2 archive. File Name: HUSINTRO.TXTResource Description: Included in ALLWEST archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original title page for HUSSI. File Name: TITLPAGE.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original statistics file for HUSSI records . File Name: HUSSTAT.TXTResource Description: A description of record types for Hopkins U.S. System files and number of HUSSI records for each repository as of March 1991. Part of the ALLWEST2 archive downloaded October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This is the original dataset for ID 41 Ta in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/41). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

This is the original dataset for ID 79 Ta in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/79). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

This is the original dataset for ID 72 Zr in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/72). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

This is the original dataset for ID 80 Re in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/80). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

Comprehensive dataset of 1 Lodgings in MDR, Philippines as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

226 Global import shipment records of Mdr Switching with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The former Techniques Development Laboratory (TDL) of the U.S. National Weather Service began collecting Manually-Digitized Radar (MDR) data in November 1973 from teletype reports. The MDR data were then archived in Model Output Statistics (MOS) predictand tape format to be used for thunderstorm prediction. The data were intended for both general and severe thunderstorm prediction using MOS which related the radar data to large-scale predictors from operational numerical models. Other uses of the data included development of improved initial moisture fields in TDL numerical models and verification of convective weather forecasts. The MDR data were archived for a pseudo-grid covering roughly the eastern two-thirds of the United States. This pseudo-grid was a subset of the 47 by 51 (octagonal) polar-stereographic grid that was used for numerical weather output at the time.

This is the original dataset for ID 24 Ru in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/24). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

The Electronic Device History Record (eDHR) solution market is experiencing robust growth, driven by increasing regulatory compliance needs within the medical device and pharmaceutical industries. Stringent regulations like FDA 21 CFR Part 11 and EU MDR necessitate comprehensive and auditable records for medical devices throughout their lifecycle, fueling demand for sophisticated eDHR solutions. This demand is further amplified by the rising adoption of cloud-based solutions offering scalability, accessibility, and cost-effectiveness compared to on-premise systems. The market is segmented by application (medical, diagnostic, other) and deployment type (cloud-based, on-premise), with the cloud-based segment projected to dominate due to its inherent advantages. Key players such as Siemens, MasterControl, and Greenlight Guru are actively investing in research and development to enhance their offerings and cater to the evolving market needs, including integration with other enterprise systems for improved data management and traceability. North America currently holds the largest market share, owing to the strong regulatory environment and high adoption rates among medical device manufacturers. However, regions like Asia Pacific are witnessing rapid growth due to increasing healthcare spending and expanding medical device manufacturing bases. The competitive landscape is characterized by a mix of established players and emerging technology providers, leading to innovation and increased solution availability. This dynamic market is expected to continue its growth trajectory throughout the forecast period. The projected CAGR of the eDHR market, based on industry analysis and considering typical growth rates in related sectors, is estimated to be around 15% from 2025-2033. This robust growth is influenced by several factors, including ongoing digitization within healthcare, the increasing complexity of medical devices requiring enhanced traceability, and the rising prevalence of data breaches necessitating secure data management practices. While the initial investment in implementing eDHR systems can be substantial, the long-term benefits of improved compliance, reduced operational costs, and enhanced product safety outweigh the upfront expenses. Future growth will likely be shaped by advancements in artificial intelligence (AI) and machine learning (ML) for improved data analysis and predictive maintenance, along with greater integration with other digital health platforms. Competition is likely to intensify, driving further innovation and potentially leading to more cost-effective solutions.

BackgroundIn recent years, drug resistant tuberculosis (DR-TB) particularly the emergence of multi-drug-resistant tuberculosis (MDR-TB) has become a major public health issue. The most recent study regarding the prevalence of drug-resistant tuberculosis in mainland China was a meta-analysis published in 2011, and the subjects from the included studies were mostly enrolled before 2008, thus making it now obsolete. Current data on the national prevalence of DR-TB is needed. This review aims to provide a comprehensive and up-to-date assessment of the status of DR-TB epidemic in mainland China.MethodsA systematic review and meta-analysis of studies regarding the prevalence of drug-resistant tuberculosis in mainland China was performed. Pubmed/MEDLINE, EMBASE, the Cochrane central database, the Chinese Biomedical Literature Database and the China National Knowledge Infrastructure Database were searched for studies relevant to drug-resistant tuberculosis that were published between January 1, 2012 and May 18, 2015. Comprehensive Meta-Analysis (V2.2, Biostat) software was used to analyse the data.ResultsA total of fifty-nine articles, published from 2012 to 2015, were included in our review. The result of this meta-analysis demonstrated that among new cases, the rate of resistance to any drug was 20.1% (18.0%–22.3%; n/N = 7203/34314) and among retreatment cases, the rate was 49.8% (46.0%–53.6%; n/N = 4155/8291). Multi-drug resistance among new and retreatment cases was 4.8% (4.0%–5.7%; n/N = 2300/42946) and 26.3% (23.1%–29.7%; n/N = 3125/11589) respectively. The results were significantly heterogeneous (p

The Medical Vigilance Solutions market is experiencing robust growth, driven by increasing regulatory scrutiny, the rising incidence of adverse events related to pharmaceuticals and medical devices, and the expanding adoption of advanced technologies like AI and machine learning for faster and more efficient signal detection. The market, estimated at $5 billion in 2025, is projected to exhibit a healthy CAGR (let's assume 7% for this example, a reasonable figure given the industry's growth trajectory) over the forecast period (2025-2033). This growth is fueled by several key trends, including the increasing outsourcing of medical vigilance activities by pharmaceutical and medical device companies, the growing demand for comprehensive data management and analysis solutions, and the expanding use of cloud-based platforms to enhance data accessibility and collaboration. Furthermore, the rise in personalized medicine and the complexity of modern healthcare technologies contribute significantly to the demand for sophisticated medical vigilance solutions. Significant market segments include clinical and non-clinical applications, with clinical applications dominating due to stringent regulatory requirements. Within these applications, services related to writing and submitting documents and reports, as well as security database system services and data management, constitute substantial portions of the market. The North American market currently holds a significant share, attributed to robust regulatory frameworks and a high concentration of pharmaceutical and medical device companies. However, regions like Asia Pacific are poised for significant growth, driven by increasing healthcare expenditure and rising awareness of medical vigilance practices. While the market faces constraints such as the high cost of implementing and maintaining sophisticated medical vigilance systems and the complexities associated with data integration and interoperability, the overall outlook remains positive, with substantial growth opportunities for market players in the coming years.

Comprehensive dataset of 1 Hotels in MDR, Philippines as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.