ASPECTS, Large Vessel Occlusion, and Time of Symptom Onset: Estimation of Eligibility for Endovascular Therapy
Bibliographic record
Abstract
BACKGROUND: Understanding how many patients are eligible for endovascular therapy can help develop more effective stroke systems of care. OBJECTIVE: To determine the proportion of patients eligible for endovascular therapy. METHODS: In this single center retrospective cohort study, we identified patients with acute ischemic stroke from large vessel occlusion (LVO) from January 2014 to December 2015. Selection criteria including LVO location, Alberta stroke program early computed tomography score (ASPECTS) range, hospital arrival time, and the use of intravenous thrombolysis were applied to calculate proportions of patients eligible for endovascular therapy. RESULTS: Of 989 patients with acute ischemic stroke, LVO was identified in 224 (23%) cases. Eighty-four percent of patients admitted within 6 h, 75% of patients admitted within 6 to 12 h, and 77% of patients with M1 and internal carotid artery occlusions admitted within 12 to 24 h had favorable ASPECTS for thrombectomy, defined as ASPECTS ≥ 6. Severity of NIHSS (National Institutes of Health Stroke Scale) was predictive of favorable ASPECTS (odds ratio [OR] 0.20, 95% confidence interval [CI] 0.04-0.74; P = .014 for NIHSS > 10 and OR 0.30, 95% CI 0.12-0.80; P = .014 for NIHSS > 20), whereas time of hospital arrival was not (OR 1.73, 95% CI 0.76-4.03; P = .26). Using all class I, Level of Evidence A American Heart Association guidelines criteria, 4% of patients with acute ischemic stroke were eligible for endovascular therapy. Up to 20% of patients were eligible for endovascular therapy with less restrictive criteria (Class IIb; Level of Evidence C). CONCLUSION: Depending on the sets of selection criteria, between 4% (the most restrictive criteria) and 20% (the least restrictive criteria) of patients with LVO are potentially eligible for endovascular therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".