Delay between Stroke Onset and Emergency Department Evaluation
Bibliographic record
Abstract
BACKGROUND: Public educational programs have been developed to reduce delays between the onset of ischemic stroke symptoms and emergency department evaluation. An increase in the proportion of patients presenting soon after stroke would reflect the effectiveness of these efforts. METHODS: All patients (n = 506) with ischemic stroke admitted to an academic medical center located within the 'Stroke Belt' of the USA were prospectively identified over 2 years (1998-1999). Demographics, stroke characteristics and time from symptom onset to arrival in the emergency department were recorded. RESULTS: A higher proportion of ischemic stroke patients presented within 3 h of symptoms in 1998 than in 1999 (18% of 234 vs. 8% of 272, p = 0.0001). Those with less severe strokes (Canadian Neurological Scale score; Spearman r = 0.18, p < 0.0001) and younger patients (r = -0.09, p = 0.04) had greater delays. There was no difference in time to presentation based on race (13% of whites and blacks presented within 3 h, p = 0.70) or sex (16% of women vs. 9% of men, p = 0.10). Logistic regression showed that time to presentation was independently related to both stroke severity and year. CONCLUSIONS: These data show that, after accounting for other variables, the proportion of stroke patients presenting within 3 h of symptom onset to one academic medical center decreased by 10% between 1998 and 1999. Revision of public stroke-related educational programs may need to be considered.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".