Postmarket Modifications of High-risk Plastic Surgery Devices
Bibliographic record
Abstract
BACKGROUND: In the United States, high-risk medical devices must be cleared through the premarket approval (PMA) pathway, which requires clinical evidence ensuring safety and efficacy. Approved devices can be modified and reintroduced to market without additional study through the PMA supplemental review track. This study characterizes the changes of high-risk plastic surgery devices once they undergo initial clearance. METHODS: A retrospective, cross-sectional analysis of the Food and Drug Administration (FDA) PMA database. The following data were extracted from the PMA database (January 1, 1980 to December 31, 2018): initial clearance date, device type, the number and type of supplement, supplement reason, and product withdrawal date. Data from the FDA medical device recall database were also extracted and reported. The median number of device modifications and median lifetime of device-years were calculated. RESULTS: = 0.000). Overall, approved plastic surgery devices have undergone a median of 11 changes (IQR, 3-35). Breast implant devices collectively underwent the most modifications with a median of 28 modifications per device (IQR, 20.25-33.25). CONCLUSIONS: Over the past 2 decades, plastic surgery device manufacturers have significantly increased the use of supplement track review. High-risk plastic surgery devices may undergo frequent minor changes without clinical evidence to support the safety and efficacy of modified versions.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".