A Model of Care for Osteoarthritis of the Hip and Knee: Development of a System-Wide Plan for the Health Sector in Victoria, Australia
Bibliographic record
Abstract
Osteoarthritis (OA) imposes a significant burden to the person, the health system and the community.Models of Care (MoCs) drive translation of evidence into policy and practice and provide a platform for health system reform.The Victorian MoC for OA of the hip and knee was developed following a best-practice framework, informed by best-evidence and iterative cross-sector consultation, including direct consumer consultation.Governance and external expert advisory committees consisting of local OA care champions facilitated the development and consultation processes.The MoC outlines key components of care, care that is not recommended, and suggests phased implementation strategies.This paper describes the MoC development process and lessons learned. RésuméL' arthrose est un lourd fardeau pour les personnes, le système de santé et la communauté.Les modèles de soin (MdS) permettent de transposer les données en politique ou en pratique, en plus d' offrir une plateforme pour la réforme du système de santé.Le MdS de Victoria pour l' arthrose de la hanche et du genou a été développé en suivant un cadre de pratique exemplaire, en tenant compte des meilleures données et en menant des consultations intersectorielles itératives, notamment auprès de la clientèle.Le développement et la consultation ont été facilités par des comités de gouvernance et d' experts-conseils externes formés de champions locaux des soins pour l' arthrose.Le MdS présente les éléments clés des soins, relève les soins non recommandés et propose des stratégies de mise en œuvre par phases successives.Cet article décrit le processus de développement du MdS ainsi que les leçons qui en ont été tirées.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".