The Problem of Restrictive Thrombectomy Trial Eligibility Criteria
Bibliographic record
Abstract
Since 2015, a series of endovascular trials transformed the management of patients with large vessel occlusion stroke. Most thrombectomy trials used restrictive eligibility criteria to optimize the chances of showing that thrombectomy could work. The problem arises when generalizing trial results into evidence-based recommendations. Many organizations, oblivious of this problem, translated verbatim restrictive trial eligibility criteria into authoritative guidelines, which limit the use of thrombectomy to highly selected patients. The clinical problem becomes as follows: what to do for all other stroke patients equally in need of care? The cycle of restrictive trial eligibility criteria, corresponding restrictive guidelines, observational studies of unvalidated practices showing other patients benefit, a new trial is needed, has been repeated often. Thrombectomy trials ought to have included all patients that could potentially benefit. If the signal that was looked for by restricting eligibility is at risk of being lost in the noise generated by the heterogeneity of patients, D. Sackett proposed a solution: to use the same criteria, not to select some patients and exclude others but to prespecify the subgroup of patients most likely to benefit. In this commentary, we propose a tiered approach, where the boundaries of treatment beneficiaries can be more rigorously tested and confirmed. Identification of these patients before the development of guidelines, which would have otherwise neglected these individuals, may open innumerable treatment opportunities to those who will instead be denied of them.
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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 | 0.229 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.007 | 0.015 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".