P.064 Delays in the emergency department for stroke patients, medical complications and predictors of outcomes: the McGill experience
Bibliographic record
Abstract
Background: The Canadian Stroke Best Practice recommends admission of patients to a specialised stroke unit within three hours. We aimed at assessing delays in our emergency department (ED) and correlating these with medical complications and clinical outcomes. Methods: Predictors and outcomes This is a retrospective review of patients (n=353) admitted with ischemic strokes (January 2011-March 2014). We assessed the length of stay in ED, medical complications in ED and in the stroke unit, functional status (modified Rankin Scale) at discharge and survival. Results: The median delay in ED was 13.8 hours. The rate of medical complications in the ED was 14% (most common being delirium), compared to the stroke unit with 46.7% (most common being pneumonia). Worse functional outcome was correlated with diagnosis of pneumonia (standardised β coefficient=0.2, p=0.001) and presence of brain oedema in the stroke unit (standardised β coefficient=0.2, p<0.01). Increased risk of death was correlated with brain oedema (OR=649.2, 95%CI=19-2184, p<0.01) and sepsis in the stroke unit (OR=26.8, 95%CI=2.1-339, p<0.01). Conclusions: We found a significant delay in the admission of our patients from the ED to the stroke unit, which is not in keeping with the present guidelines. Medical complications were correlated with worse outcomes. Future analyses will correlate ED delays with clinical outcomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".