Immediate Vascular Imaging Needed for Efficient Triage of Patients With Acute Ischemic Stroke Initially Admitted to Nonthrombectomy Centers
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Current guidelines for endovascular thrombectomy (EVT) used to select patients for transfer to thrombectomy-capable stroke centers (TSC) may result in unnecessary transfers. We sought to determine the impact of simulated baseline vascular imaging on reducing unnecessary transfers and clinical-imaging factors associated with receiving EVT after transfer. METHODS: We identified patients with stroke transferred for EVT from 30 referring hospitals between 2010 and 2016 who had a referring hospitals brain computed tomography and repeat imaging on TSC arrival available for review. Initial Alberta Stroke Program Early CT scores and TSC vascular occlusion level were assessed. The main outcome variable was receiving EVT at TSC. Models were simulated to derive optimal triaging parameters for EVT. RESULTS: A total of 508 patients were included in the analysis (mean age, 69±14 years; 42% women). Application at referring hospitals of current guidelines for EVT yielded sensitivity of 92% (95% confidence interval, 0.84-0.96) and specificity of 53% (95% confidence interval, 0.48-0.57) for receiving EVT at TSC. Repeated simulations identified optimal selection criteria for transfer as National Institute of Health Stroke Scale >8 plus baseline vascular imaging (sensitivity=91%; 95% confidence interval, 0.83-0.95; and specificity=80%; 95% confidence interval, 0.75-0.83). CONCLUSIONS: Our findings provide quantitative estimates of the claim that implementing vascular imaging at the referring hospitals would result in significantly fewer futile transfers for EVT and a data-driven framework to inform transfer policies.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".