Trial selection criteria should not be used for clinical decisions and recommendations: the thrombectomy trials example
Bibliographic record
Abstract
• Legitimate reasons for restricting trial eligibility are reviewed. • Most thrombectomy trials have been too restrictive, leading to countless acute stroke patients being denied effective, life-saving treatment. • Trial eligibility criteria cannot be used to make recommendations unless they have been shown to reliably categorize patients according to treatment effect. Despite multiple calls for more inclusive studies, most clinical trial eligibility criteria remain too restrictive. Thrombectomy trials have been no exception. We review the landmark trials that have shown the benefits of thrombectomy, their eligibility criteria, and consequences on clinical practice. We discuss the rationale behind various reasons for exclusions. We also examine the logical problem involved in using eligibility criteria as indications for treatment. Most thrombectomy trials have been too restrictive. This has been shown by a plethora of follow-up studies that have refuted most of the previously recommended trial eligibility restrictions. Meanwhile, the effect of clinical recommendations based on restrictive eligibility criteria is that treatment has been denied to the majority of patients who could have benefitted. Trial eligibility criteria cannot be used to make clinical decisions or recommendations unless, like any other medical diagnosis, they have been shown capable of reliably differentiating patients into those that will, and those that will not benefit from treatment. This goal can only be achieved with all-inclusive pragmatic trials. Restrictive eligibility criteria render clinical trials incapable of guiding medical decisions or recommendations.
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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 | 0.184 | 0.416 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".