Abstract TP361: Clinical Integration of Stroke Services to Improve Access to Quality Stroke Care in Waterloo/Wellington Local Health Integration Network
Bibliographic record
Abstract
Background: Stroke is the third most common cause of death in the Waterloo Wellington Local Health Integration Network (WWLHIN) area. By 2024, the number of stroke in the WWLHIN will increase 50% as the population grows and ages. The WWLHIN has partnered with the Ontario Stroke Network (OSN) to complete an external review of stroke care in Waterloo Wellington with a goal to enhance local stroke care programming by aligning the present model with the 2010 Canadian Stroke Best Practice Guidelines. Purpose: The purpose of the review of stroke care in WWLHIN was to improve access to best practice stroke unit care and provide seamless transitions across the continuum of care and improve patient outcomes. Methods: Comprehensive research was completed including, data collection, literature review, and public consultation. In addition, focus groups were conducted to identify key issues and opportunities for improving care. A decision making framework was used to support the siting and sizing of stroke beds in WW LHIN. Results: Clinical integration of stroke services across WWLHIN will be implemented to create specialized stroke units with sufficient critical mass that will allow geographically designated units staffed with specialized and dedicated interdisciplinary teams. Stroke services will be consolidated to 3 specialized stroke units for acute and rehabilitative care from 8 sites of generalized medical care. Access to the appropriate level of care will be determined based on standardized, best practice assessment tools and there will no longer be an application process for rehabilitative care. Community Integration Teams are being developed in 4 communities across the WWLHIN to facilitate early supported discharge and seamless transition into the community. Conclusions: The newly designed Quality Stroke System in WWLHIN will provide every stroke patient with access to best practice stroke unit care and timely access to early intensive therapy. Clinical integration of stroke services and relocation of stroke beds will occur to achieve critical mass. Patients will receive the right care in the right place at the right time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".