Performance and training standards for endovascular ischemic stroke treatment
Bibliographic record
Abstract
Stroke is the third leading cause of death in the USA, Canada, Europe, and Japan. According to the American Heart Association and the American Stroke Association, there are now 750,000 new strokes that occur each year, resulting in 200,000 deaths, or 1 of every 16 deaths, per year in the USA alone. Endovascular therapy for patients with acute ischemic stroke is an area of intense investigation. The American Stroke Association has given a qualified endorsement of intra-arterial thrombolysis in selected patients. Intra-arterial thrombolysis has been studied in two randomized trials and numerous case series. Although two devices have been granted FDA approval with an indication for mechanical stroke thrombectomy, none of these thrombectomy devices has demonstrated efficacy for the improvement of patient outcomes. The purpose of the present document is to define what constitutes adequate training to perform neuroendovascular procedures in patients with acute ischemic stroke and what performance standards should be adopted to assess outcomes. These guidelines have been written and approved by multiple neuroscience societies which historically have been directly involved in the medical, surgical and endovascular care of patients with acute stroke. The participating member organizations of the Neurovascular Coalition involved in the writing and endorsement of this document are the Society of NeuroInterventional Surgery, the American Academy of Neurology, the American Association of Neurological Surgeons/Congress of Neurological Surgeons Cerebrovascular Section, and the Society of Vascular & Interventional Neurology.
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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 | 0.044 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".