Introduction of the chronic care model into an academic rheumatology clinic
Bibliographic record
Abstract
BACKGROUND: While the chronic care model has been extensively used for the management of patients with diabetes in non-academic, primary care settings, it is not clear whether this model can be used effectively in academic, specialty clinics for other chronic disorders. METHODS: Through the Academic Chronic Care Collaborative, the chronic care model was introduced to help manage patients with osteoarthritis in an academic rheumatology service with seven prespecified goals. These goals included measurements of Western Ontario MacMaster (WOMAC) osteoarthritis scores, self-efficacy scores and exercise time. RESULTS: Five a priori goals were achieved in this study: average WOMAC scores less than 1000 mm as measured on a visual analogue scale, average self-efficacy score of less than 5 mm, average exercise time greater than 90 min, more than 40% of patients exercising at least 60 min per week and a 20% improvement in self-efficacy scores. However, a 20% improvement in WOMAC scores and a 60% completion of documented self-management goals in our patients were not achieved. Our inability to achieve our self-management goal underscores the fact that we have not yet fully implemented the chronic care model into our practice. The inability to detect a 20% improvement in WOMAC scores in the context of having reached our absolute WOMAC goal at baseline suggests a probable ceiling effect for this measure. CONCLUSIONS: The chronic care model can be effectively introduced into an academic specialty service and can be used effectively in the management of patients with non-diabetic disorders, in this case osteoarthritis.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".