Proportion of patients with hip osteoarthritis in primary care identified by differing clinical criteria: a cross-sectional study of 4699 patients
Bibliographic record
Abstract
Objective: Differing clinical criteria for hip osteoarthritis (OA) are applied in primary care, but little is known regarding the utility of these criteria. The aim of this study was to evaluate and compare the proportion of patients in a primary care setting with hip OA fulfilling the American College of Rheumatology (ACR), the National Institute for Health and Care Excellence (NICE), and the Danish Health Authority (DHA) criteria. Design: A cross-sectional analysis of baseline data from the Good Life with osteoArthritis in Denmark (GLA:D®) program, a treatment program for patients with symptoms or functional limitations associated with hip OA. The prevalence of hip OA according to the ACR, NICE, and DHA criteria was calculated in all patients and in a subgroup of patients with self-reported radiographic hip OA. Results: 4699 patients were included in the analysis. Mean age (SD) was 66.8 (9.7) years and 71% of the patients were female. 64%, 80%, and 94% fulfilled the ACR, DHA, and NICE criteria, respectively. In those self-reporting radiographic hip OA, the corresponding numbers were 66%, 81%, and 94%. A limited number of patients (4%) did not fulfill any of the criteria. Conclusions: The NICE criteria identified the most patients that were treated because of their symptoms or functional limitations. The DHA and especially the ACR criteria did not identify a significant proportion of these patients. The results suggest the NICE criteria are appropriate to identify individuals treated for hip OA in primary 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".