Toward a Joint Health and Disease Management Program - Toronto Hospitals Partner to Provide System Leadership
Bibliographic record
Abstract
The Joint Health and Disease Management Program in the Toronto Central Local Health Integration Network (TC LHIN) is envisioned as a comprehensive model of care for patients with hip and knee arthritis. It includes access to assessment services, education, self-management programs and other treatment programs, including specialist care as needed. As the first phase of this program, the hospitals in TC LHIN implemented a Hip and Knee Replacement Program to focus on improving access and quality of care, coordinating services and measuring wait times for patients waiting for hip or knee replacement surgery. The program involves healthcare providers, consumers and constituent hospitals within TC LHIN. The approach used for this program involved a definition of governance structure, broad stakeholder engagement to design program elements and plans for implementation and communication to ensure sustainability. The program and approach were designed to provide a model that is transferrable in its elements or its entirety to other patient populations and programs. Success has been achieved in creating a single wait list, developing technology to support referral management and wait time reporting, contributing to significant reductions in waits for timely assessment and treatment, building human resource capacity and improving patient and referring physician satisfaction with coordination of care.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".