A cross-sectional study of USFDA warning letters issued for cGMP violations pertaining to medical devices
Bibliographic record
Abstract
Most of the prior studies concentrated on warning letters issued for clinical investigation, Institutional review board, and infringement of promotional claims, no studies assessed the warning letters issued for infringements of Current Good Manufacturing Practice (cGMP) pertaining to medical devices. Hence, there is a need to carry out a crosssectional study of these warning letters. Publically available U.S. Food & Drug Administration (USFDA) letters under the law of the freedom of Information Act sent to the pharmaceutical company were accessed from the USFDA website. A standard data collection tool (Excel Spreadsheet) with all letters of warning issued from January 2008 to July 2018 was developed. Letters have been manually screened. Warning letters related to medical device breaches of cGMP were screened based on the letter's subject and content. Overall, 669 warning letters issued for medical device cGMP violations were reviewed between January 2008 and November 2018. From 2008 to 2013, there was a downward trend in the issuance of warning letters. The number of warning letters issued in 2014 was 101, followed by 106 in 2015, as the USFDA focused more on data integrity issues, while the number decreased to 53, 27, and 19, respectively, in 2016, 2017, and 2018. The highest number of warning letters were issued to manufacturers located in the USA (379), followed by Canada (52), and China (37). Section 820.30 of Title 21 CFR was found to be most violated with 603 infringements. This section represents the design control requirements for cGMP. Class 2 type of medical devices were found to be most violated (82%), followed by Class 3 with 7%. Only 32% of the companies responded to the warning letters although 52% Not Issued the closeout letter followed by 16% of the letters were considered as non-applicable letters. With the time, scientific developments and increased awareness of both regulatory authorities and industries/ academic organizations, overall improvement are observed with significant decrease in the number of warning letters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".