In-Patient Code Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke is a relatively common and challenging condition in hospitalized patients. Previous studies have shown delays in recognition and assessment of inpatient strokes leading to poor outcomes. The goal of this quality improvement initiative was to evaluate an in-hospital code stroke algorithm and educational program aimed at reducing the response times for inpatient stroke. METHODS: An inpatient code stroke algorithm was developed, and an educational intervention was implemented over 5 months. Data were recorded and compared between the 36-month period before and the 15-month period after the intervention was implemented. Outcome measures included time from last seen normal to initial assessment and from last seen normal to brain imaging. RESULTS: During the study period, there were 218 inpatient strokes (131 before the intervention and 87 after the intervention). Inpatient strokes were more common on cardiovascular wards (45% of cases) and occurred mainly during the perioperative period (60% of cases). After implementation of an inpatient code stroke intervention and educational initiative, there were consistent reductions in all timed outcome measures (median time to initial assessment fell from 600 [109-1460] to 160 [35-630] minutes and time to computed tomographic scan fell from 925 [213-1965] to 348.5 [128-1587] minutes). CONCLUSIONS: Our study reveals the efficacy of an inpatient code stroke algorithm and educational intervention directed at nurses and allied health personnel to optimize the prompt management of inpatient strokes. Prompt assessment may lead to faster stroke interventions, which are associated with better outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".