Extended Delays in Recognition of Stroke Symptoms and Stroke Code Activation for In-Hospital Strokes: The DELAY Study
Bibliographic record
Abstract
ABSTRACT Background: Patients with stroke while hospitalized experience important delays in symptom recognition. This study aims to describe the overall management of an in-hospital stroke population and how it compares with an out-of-hospital community-onset stroke population. Methods: In this retrospective observational study, we included consecutive patients with in-hospital and out-of-hospital strokes (both ischemic and hemorrhagic) over a period of one year treated at a comprehensive stroke center. Demographic and clinical data were extracted, and patient groups were compared with regard to stroke treatment time metrics. Results: A total of 362 patients diagnosed with acute stroke were included, of whom 38 (10.5%) had in-hospital and 324 (89.5%) had out-of-hospital strokes. The median delay to stroke recognition (time between the last time seen well and first time seen symptomatic) was significantly longer in in-hospital compared to out-of-hospital strokes (77.5 [0–334.8] vs. 0 [0–138.5] min, p = 0.04). The median time interval from stroke code activation to the arrival of the stroke team at the bedside was significantly shorter in in-hospital versus out-of-hospital cases (10 [6–15] vs. 15 [8–24.8] min, p = 0.01). In-hospital strokes were less likely to receive thrombolysis (12.8% vs. 45.4%, p < 0.01) with significantly higher mortality (18.2% versus 2.6%, p < 0.01) and longer overall median hospital stay (3 [1–7] vs. 12 days [7–23], p < 0.01) compared to out-of-hospital strokes. Conclusion: This study showed significant delays in stroke symptom recognition and stroke code activation for in-hospital stroke patients despite comparable overall stroke time metrics. Development of in-hospital stroke protocols and systematic staff training on stroke symptom recognition should be implemented to improve care for hospitalized patients.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".