B.5 Video-based prehospital teletriage for acute stroke: primary results from a regional pilot-study
Bibliographic record
Abstract
Background: Only limited data exist on the potential benefit of prehospital video-based teletriage for patients with acute stroke. Methods: During a 6-month period, all patients from a defined geographical catchment area with a 911 call for acute stroke were screened by the paramedic team on site. Those with known symptom onset of <6h underwent video-based teletriage for transfer to either the closest tertiary (for suspected LVO occlusion) or to the closest secondary stroke centers. Patients referred for thrombectomy by same the secondary stroke centers without teletriage during the same period served as control. Results: Overall, 33 patients were teletriaged and 23 (70%) were bypassed to the tertiary center. Of the latter, 13 (median NIHSS 19) underwent thrombectomy (+/- iv thrombolysis). During the same period, 22 patients (median NIHSS 17) were referred for thrombectomy without teletriage. The median time from 911 to thrombectomy was 129 [IQR 51] min after teletriage, as compared to 196 [74] min in controls (p=0.015). The median NIHSS at 24h was 6 in the teletriage group versus 14.5 in controls (p=0.07). Conclusions: Video-based prehospital teletriage for acute stroke is feasible, reliably identifies patients without LVO stroke and significantly improves the delay between stroke alert and thrombectomy in eligible LVO stroke patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".