The CAPTURE registry: Results of carotid stenting with embolic protection in the post approval setting
Bibliographic record
Abstract
BACKGROUND: Pivotal study data examining carotid stenting with embolic protection as a less invasive alternative to endarterectomy for high surgical risk patients have been acquired under controlled conditions with highly selected physicians and hospitals. This report examines outcomes of carotid stenting post-approval after diffusion of this technology to a broader cross-section of physicians and hospitals. METHODS: The Carotid Acculink/Accunet Post-Approval Trial to Uncover Unanticipated or Rare Events (CAPTURE) is a prospective, multi-center registry to assess two important aspects of the post-IDE experience: the safety of carotid stenting by physicians with varying levels of experience as a measure of the adequacy of physician training, and the identification of rare/unexpected device-related complications. The primary endpoint was a composite of death, any stroke, or myocardial infarction within 30 days post-procedure. RESULTS: 3,500 patients were enrolled by 353 physicians at 144 sites. The 30-day primary endpoint event rate was 6.3% (95% CI: 5.5-7.1%) and did not differ among the three operator experience levels (5.3%, 6.0%, and 7.4%; P = 0.31) from most to least experienced, respectively. There were no differences in outcomes among physician specialties when adjusted for case mix. There were no unanticipated device related adverse events. CONCLUSIONS: The results of the CAPTURE study compare favorably to those achieved in the predicate pivotal investigations, and suggest that the post-approval transfer of this new therapy to the community practice setting via carotid stent training programs is effective in preparing physicians with varying experience levels and specialty training backgrounds.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".