Characteristics of pivotal clinical trials of FDA-approved endovascular devices between 2000 and 2018: An interrupted time series analysis
Bibliographic record
Abstract
Background: The Food and Drug Administration (FDA) reviews safety, efficacy, and the quality of medical devices through its regulatory process. The FDA Safety and Innovation Act (FDASIA) of 2012 was aimed at accelerating the regulatory process for medical devices. Objectives: The purpose of our study was to (1) quantify characteristics of pivotal clinical trials (PCTs) supporting the premarket approval of endovascular medical devices and (2) analyze trends over the last two decades in light of the FDASIA. Methods: We surveyed the study designs of endovascular devices with PCTs from the US FDA pre-market approval medical devices database. The effect of FDASIA on key design parameters (e.g., randomization, masking, and number of enrolled patients) was estimated using an interrupted time series analysis (segmented regression). Results: < 0.0001). Discussion: Our results reveal an overall trend of decreased regulatory requirements as it relates to clinical trial characteristics, but a compensatory increased rate of post-approval across device classes. Furthermore, there was an emphasis on proving equivalence or non-inferiority rather than more use of active comparators in clinical trials. Medical device stakeholders, notably clinicians, must be aware of the shifting regulatory landscape in order to play an active role in promoting patient safety.
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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 | 0.068 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".