Prehospital Stroke Notification And Endovascular Therapy For Large Vessel Occlusion: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract The impact of prehospital notification by emergency medical services (EMS) on outcomes of endovascular therapy (EVT) for large vessel occlusion (LVO) remains unclear. We therefore explored the association between prehospital notification and clinical outcomes after EVT. In this single-center retrospective study from 2016 through 2020, we identified all LVO patients who received EVT. Based on the EMS’s usage of a prehospital stroke notification system, we categorized patients into two groups, Hotline and Non-hotline. The primary outcome was good neurological outcome at 90 days; other time metrics were also evaluated. Of all 312 LVO patients, the proportion of good neurological outcomes was 94/218 (43.1%) in the Hotline group and 8/34 (23.5%) in the Non-hotline group (adjusted odds ratio 2.86; 95% confidence interval 1.12 to 7.33). Time from hospital arrival to both tissue plasminogen activator and to groin puncture were shorter in the Hotline group (30 (24 to 38) min vs 48(37 to 65) min, p < 0.001; 40 (32 to 54) min vs 76 (50 to 97) min, p < 0.001), respectively. In conclusion, prehospital notification was associated with a reduction in time from hospital arrival to intervention and improved clinical outcomes in LVO patients treated with EVT. Non-standard Abbreviations and AcronymsEVT, endovascular therapy; t-PA, tissue plasminogen activator; LVO, large vessel occlusion; EMS, emergency medical services; mRS, modified Rankin scale; NIHSS, National Institute of Health Stroke Scale; ASPECTS, Alberta stroke program early computed tomography score; TICI, thrombolysis in cerebral infarction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".