7 Identifying Patients for Stroke ThrombectomyStroke thrombectomy, identifying patients for
Bibliographic record
Abstract
Identification of patients for stroke thrombectomy begins with a proper diagnosis of acute ischemic stroke (AIS). Upon suspicion of AIS, timely communication (including through telehealth) between paramedics, emergency department staff, and stroke specialists is the key to accelerate triage of AIS patients. Determining the last known normal (LKN) status includes obtaining a thorough history, conducting a physical examination, and determining the National Institutes of Health Stroke Scale (NIHSS) score. This will help further categorize the severity of the stroke and, in conjunction with imaging findings, will determine appropriate treatment options. The purpose of imaging is to (1) exclude the presence of hemorrhage, (2) rule out other pathologies that may mimic an AIS, (3) determine the location and extent of the occlusion, and (4) estimate tissue at risk in patients presenting 6 to 24 hours after LKN finding of large vessel occlusion. Alberta Stroke Program early CT Score (ASPECTS) > 6, NIHSS > 6, and parameters satisfying adequate tissue at risk or salvageable penumbra (for 6- to 24-hour time window) are the main eligibility criteria for mechanical thrombectomy (MT). Penumbra volume can be calculated by automated software that allows for faster interpretation of imaging findings. Multiple studies are challenging the current eligibility criteria dictated by the American Heart Association/American Stroke Association (AHA/ASA) guidelines and are demonstrating promising results in patients otherwise noneligible for MT such as patients with low NIHSS, low ASPECTS, and patients presenting beyond the 24-hour window of opportunity. These efforts will potentially widen the eligibility criteria for MT and bring further changes to current AIS guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".