P.067 Incidence of tissue-defined stroke and large vessel occlusion in acute stroke alerts in a non-teaching hospital system
Bibliographic record
Abstract
Background: Stroke alerts are used to triage patients with acute neurologic change for rapid imaging evaluation. CTA has been advocated to rapidly triage stroke patients for endovascular therapy. However, the yield of this approach is not well established. We evaluated the stroke alert yield in a non-teaching hospital system. Methods: A retrospective review of radiology reports for stroke alerts using PACS archive. Cases were then followed for 72 hours to determine the types of advanced imaging obtained and the findings of those studies. Results: From January to March 2014, 269 stroke alert head CTs were performed. Subsequent imaging included 128 MRIs (48%), 25 CTAs (9%) and 2 angiograms (0.7%). There were 58 (22%) tissue-defined strokes and 16 were non-lacunar (6% stroke alerts). 61% of stroke alert head CTs were negative or reported microvascular change. Other findings included large vessel occlusion (5%), intracranial stenosis (1.5%), extracranial stenosis(1.5 %), intracranial hemorrhage (9%) and masses (13%). Conclusions: Most stroke alerts were negative for tissue-defined stroke. Based on this data, universal use of CTA in the ER to triage patients with acute neurologic symptoms may not be appropriate. An updated triage system to facilitate endovascular rescue is being analyzed for changes to advanced imaging utilization and yield.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".