Quality indicators for care of osteoarthritis in primary care settings: a systematic literature review
Bibliographic record
Abstract
Background: Despite the high prevalence of osteoarthritis and the prominence of primary care in managing this condition, there is no systematic summary of quality indicators applicable for osteoarthritis care in primary care settings. Objectives: This systematic review aimed to identify evidence-based quality indicators for monitoring, evaluating and improving the quality of care for adults with osteoarthritis in primary care settings. Methods: Ovid MEDLINE and Ovid EMBASE databases and grey literature, including relevant organizational websites, were searched from 2000 to 2015. Two reviewers independently selected studies if (i) the study methodology combined a systematic literature search with assessment of quality indicators by an expert panel and (ii) quality indicators were applicable to assessment of care for adults with osteoarthritis in primary care settings. Included studies were appraised using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument. A narrative synthesis was used to combine the indicators within themes. Applicable quality indicators were categorized according to Donabedian's 'structure-process-outcome' framework. Results: The search revealed 4526 studies, of which 32 studies were reviewed in detail and 4 studies met the inclusion criteria. According to the AIRE domains, all studies were clear on purpose and stakeholder involvement, while formal endorsement and use of indicators in practice were scarcely described. A total of 20 quality indicators were identified from the included studies, many of which overlapped conceptually or in content. Conclusions: The process of developing quality indicators was methodologically suboptimal in most cases. There is a need to develop specific process, structure and outcome measures for adults with osteoarthritis using appropriate methodology.
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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 | 0.046 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".