Quicr-Alberta Stroke Program : Targeting inpatient Stroke Outcomes
Bibliographic record
Abstract
Background:Reperfusion strategies in acute stroke over the past decade have dramatically altered functional outcomes in stroke patients. Strategies such as QuICR have focused on increasing the number of patients that present within the time window, thereby increasing the total number of stroke patients receiving acute stroke interventions (ASI). However, the population of patients who present with acute In Hospital Strokes (IHS), have been largely excluded from educational strategies targeting outcome. Review of the outcomes of IHS, relative to u201cout of hospital strokesu201d (OHS), indicate common trends of fewer proportions treated with ASI and poorer outcomes. IHS patients who do undergo ASI have demonstrated comparably good outcomes. A related cohort is that of patients with relapsing symptoms occurring in the emergency room (IHS-Er) setting. Methods: These groups were excluded from the first phase of the ongoing QuICR initiative of the Alberta Stroke Program. A three component strategy of: 1. Data Collection; 2. Knowledge Attitude and Practices Survey (KAPS) and 3. Education targeting this population of patients was implemented in phase 2. Results: Post implementation; an increase in the number of stroke codes called and proportion of acute strokes treated that were IHS was seen ( p = 0.02 for 2017 vs 2014). Conclusion: Concerted provincial strategies can improve access of IHS to ASI at representative Comprehensive and Primary Stroke Centres.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".