B.2 Short-term outcome in simultaneous acute code stroke activations in the emergency department
Bibliographic record
Abstract
Background: We aim to assess the effect of simultaneous acute code stroke activation(ACSA) in patients undergoing reperfusion therapies in the emergency department on home time at 90 days. Methods: We assessed ACSA over 20 months from the QuICR(Quality Improvement and Clinical Research Alberta Stroke Program) Registry. We defined Simultaneous reperfusion therapy as, ACSA within 60 min of the arrival of any patient receiving intravenous thrombolysis or ACSA within 150 min of the arrival of any patient receiving endovascular thrombectomy (based on the Canadian Triage and Acuity Scale, average local door-to-needle and door-to-puncture times)Results: A total of 2607 ACSA occurred at a mean±SD of 130.8±17.1 per month during the study period. 545 (20.9%) underwent acute reperfusion therapy with a mean age of 70.6±14.2 years, 45.9%(n=254) were female and a median (IQR) NIHSS of 13(8-18). Simultaneous reperfusion therapies occurred in 189(34.6%). There was no difference in the median door-to-CT time between the simultaneous (16, 11-23 min) and non-simultaneous (15, 11–21 min, p=0.3) activations. There was no difference in the median home time at 90 days between the two groups. Conclusions: Simultaneous ACSA occurs in one-third of patients receiving acute reperfusion therapies. An optimal workflow may help mitigate the clinical and system burden associated with simultaneity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".