Development of Quality Indicators for an Integrated Approach of Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is a common cause of disability worldwide. Knee OA care is often suboptimal. A first necessary step in quality improvement is to gain a clear insight into usual care. We developed a set of evidence-based quality indicators for multidisciplinary high-quality knee OA care. METHODS: A Rand-modified Delphi method was used to develop quality indicators for knee OA diagnosis, therapy, and followup. Recommendations were extracted from international guidelines as well as existing sets of quality indicators and scored by a multidisciplinary expert panel. Based on median score, prioritization, and agreement, recommendations were labeled as having a high, uncertain, or low potential to measure quality of care and were discussed in a consensus meeting for inclusion or exclusion. Two final validation rounds yielded a core set of recommendations, which were translated into quality indicators. RESULTS: From a total of 86 recommendations and existing indicators, a core set of 29 recommendations was derived that allowed us to define high-quality knee OA care. From this core set, 22 recommendations were considered to be measurable in clinical practice and were transformed into a final set of 21 quality indicators regarding diagnosis, lifestyle/education/devices, therapy, and followup. CONCLUSION: Our study provides a robust set of 21 quality indicators for high-quality knee OA care, measurable in clinical practice. These process indicators may be used to measure usual care and evaluate quality improvement interventions across the entire spectrum of disciplines involved in knee OA 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 | 0.185 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".