Considerations and Recommendations for the Introduction of Objective Performance Criteria for Transcatheter Aortic Heart Valve Device Approval
Bibliographic record
Abstract
In the United States, new surgical heart valves can be approved on the basis of objective performance criteria (OPC). In contrast, the US Food and Drug Administration traditionally requires stricter criteria for transcatheter heart valve (THV) approval, including randomized, clinical trials. Recent US Food and Drug Administration approval of new-generation THVs based on single-arm studies has generated interest in alternative study approaches for THV device approval. This review evaluates whether THV device approval could follow a pathway analogous to that of surgical heart valves by incorporating OPC and provides several considerations and recommendations. Factors to be taken into account in the construction of OPC include the maturity of THV technology, variability in transcatheter aortic valve replacement practice, end points included as OPC, follow-up terms for specific OPC, patient populations to which these OPC apply, and (statistical) methods for OPC development. We recommend that approval of THV devices in the United States for low- and intermediate-risk patients or for new indications should provisionally rely on data from randomized, clinical trials. However, it is recommended that formal OPC be applied for approval of new-generation THVs for use in high- and extreme-risk patient populations.
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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 | 0.095 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".