Factors Related to Early Versus Late Hospital Arrival in Acute Ischemic Stroke
Bibliographic record
Abstract
Background: The onset-to-arrival time affects the decision of intravenous thrombolysis therapy which is associated with stroke’s prognosis. This study aimed to evaluate factor associated with early and late arrival of patients with Acute Ischemic Stroke and the effect on patients’ outcomes. Materials and Methods: Patients confirmed with acute ischemic stroke in central stroke centers were included in a prospective study. The patients were grouped into early arrivers (less than 4.5 hours) and late arrivers (at and after 4.5 hours). Patients’ data were obtained from the stroke registry system. Results: In Summary, Higher initial NIHSS, less than 15 km from the hospital, a history of CVA, Diabetes, Abnormal blood pressure, EMS transportation, Atrial fibrillation, and current use of anticoagulants, Not using opium, and not smoking was significantly associated with early arrival time. Normal blood pressure AF was a negative and significant (p-value=0.001, Odds ratio: 0.38) predictor of the late arrival. Normal blood pressure was a positive and significant (p-value<0.001, Odds ratio: 4.762) predictor of the late arrival. Conclusion: Some baseline factors are associated with the onset-arrival time of patients with ischemic stroke.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".