P.124 Assessing the fragility index of randomized controlled trials on carotid artery stenosis: systematic review
Bibliographic record
Abstract
Background: The fragility index (FI) is the minimum number of patients whose status would have to change from a nonevent to an event to turn a statistically significant result to a non-significant result. We used this to measure the robustness of trials comparing carotid endarterectomy (CEA) to carotid artery stenting (CAS). Methods: A search was conducted in MEDLINE, Embase, and PubMed on RCTs comparing CEA to CAS. The trials need to have statistically significant results and dichotomous primary endpoints to be included. Results: Our literature search identified 10 RCTs which included 9382 patients (4734 CEA, 4648 CAS). The primary end points of all included trials favoured CEA over CAS. The median FI was 9.5 (interquartile range 2.25 - 21.25). All of the studies that reported lost-to-follow-up (LTFU) had LTFU greater than its fragility index, which raises concern that the missing data could change the results of the trial from statistically significant to statistically insignificant. Conclusions: A small number of events (FI, median 9.5) were required to render the results of carotid artery stenosis RCTs comparing CEA to CAS statistically insignificant. All of the studies that reported LTFU had LTFU greater than its fragility index.
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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 | 0.060 | 0.313 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".