Abstract TP364: Importance of Collaboration of Hospital Leaders in Implementing Stroke Best Practice Across 17 Organizations
Bibliographic record
Abstract
Background: Literature indicates that organized acute stroke care and early access to intense rehabilitation improves patient outcomes. Toronto-wide system data demonstrated 1) Institutional variation in acute and rehabilitation stroke care performance 2) Specialized acute stroke centres perform better for mortality, readmission rates, time to rehab referral, and length of stay 3) Patients could be better streamed to appropriate rehab settings (18% of inpatient referrals were mild stroke; 3% of strokes referred to outpatient) 4) Need for higher intensity rehab post severe stroke to improve outcomes. Purpose: To re-organize the stroke system in a large city (Toronto) to increase access to acute stroke units and timely, appropriate rehabilitation while reducing variability in care. Methods: Recommendations generated from the above needs were endorsed by regional funders and organization CEOs. Key system decision-makers were engaged in planning and implementation through regional task groups. Organization-specific gap analyses and implementation plans were developed based on best practice and system needs. An evaluation framework, including targets, was created. Results: All 17 Toronto acute and rehab organizations committed in writing to a common vision of stroke care: o Reorganization of acute care to hospitals with geographical stroke units and dedicated interprofessional teams supporting evidence-based processes. o Early access to high intensity inpatient (3 hours therapy/patient/day within 5-7 days of stroke onset) and outpatient rehab (within 2 weeks of acute discharge). Organizational implementation plans are in progress. Working groups were established to address emerging system issues (e.g. Emergency walk-in patients). Conclusion: Key enablers crucial to the success of this transformational system change include: engagement of key stakeholders/leaders, grounding in best practices, based on a system perspective, supported by data, and funder endorsement. Regular monitoring and status reporting provides a foundation for ongoing continuous quality improvement and accountability. A knowledge translation strategy is being developed to support implementation.
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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 | 0.072 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".