Workflow and Short-Term Functional Outcomes in Simultaneous Acute Code Stroke Activation and Stroke Reperfusion Therapy
Bibliographic record
Abstract
The burden of simultaneous acute code stroke activation (ACSA) is not known. We aim to assess the effect of simultaneous ACSA on workflow metrics and home time at 90 days in patients undergoing reperfusion therapies in the emergency department. Simultaneous ACSA was defined as code activation within 60 min of the arrival of any patient receiving intravenous thrombolysis, within 150 min of the arrival of any patient receiving endovascular thrombectomy, within 45 min of the arrival of any patient receiving no reperfusion therapies (based on mean local door-to-needle and door-to-puncture times). Simultaneous ACSA was further graded as 1, 2 and 3. We assessed workflow metrics as door-to-CT (DTC) time, in minutes, and functional outcome as home time at 90 days. A total of 2605 patients were assessed as ACSA at a mean ± SD activations of 130.8 ± 17.1/month and 859 (33%) were simultaneous. Among all ACSA, 545 (20.9%) underwent acute reperfusion therapy with a mean age of 70.6 ± 14.2 years, 45.9% (n = 254) were female with a median (IQR) NIHSS of 13 (8–18). A total of 220 (40.4%) patients underwent simultaneous treatments. The median DTC time, in minutes, was prolonged in grade 3 simultaneous ACSA (18 (13, 28)) compared to non-simultaneous ACSA (15 (11, 21) β = 0.23, p < 0.0001). There was no difference in the median home time at 90 days between the simultaneous (58, 0–84.5 days) and non-simultaneous (54, 0–85 days) patients. Simultaneous ACSA is frequent in patients receiving acute reperfusion therapies. An optimal workflow in high-volume centers 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".