Critical Event Intervals in Determining Candidacy for Intravenous Thrombolysis in Acute Stroke
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to determine the optimal set point for the critical event benchmarks described in stroke guidelines and validate the ability of these goals to predict successful administration of intravenous thrombolysis within 60 min of hospital arrival. METHODS: This was a retrospective cohort analysis of patients with acute ischemic stroke who received intravenous thrombolysis following presentation to the emergency department. The national benchmarks for time intervals associated with the completion of critical events required to determine candidacy for thrombolysis were evaluated for the ability to predict successful administration of thrombolysis within 60 min of hospital arrival. Optimal time interval cut points were then estimated using regression and receiver-operator characteristic curve analysis and compared to guidelines. RESULTS: Of the 523 patients included in the analysis, 229 (43.8%) received intravenous thrombolysis within 60 min of hospital arrival. Of the patients who met the critical event interval goals described in guidelines, only 51.6% received thrombolysis within 60 min. The optimized cut points suggested by the regression analysis aligned with the guideline benchmarks with the only substantial difference being a shortened goal of arrival to neuroimaging start time of 19 min. This difference did not impact the overall predictive value. CONCLUSION: The critical event benchmarks proposed in this study by logistic regression closely correlate with the critical event benchmarks described in the AHA/ASA acute stroke guidelines.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".