Association between hip pain and radiographic hip osteoarthritis in primary care: the CHECK cohort
Bibliographic record
Abstract
Background The diagnosis of hip osteoarthritis (OA) is often based on clinical symptoms, such as pain and stiffness, and radiographic features. However, the association between hip pain and hip radiographic OA (ROA) remains uncertain. Aim To examine the association between hip pain and hip ROA. Design and setting Cross-sectional analysis of a Dutch cohort, the Cohort Hip and Cohort Knee (CHECK) study. Method The participants (aged 45–65 years) had all experienced hip and/or knee pain for which they had not had a prior consultation or were within 6 months of their first consultation with a GP. Using weight-bearing anteroposterior pelvis radiographs, definite and early-stage hip ROA were defined as Kellgren and Lawrence grade ≥2 and ≥1, respectively. Presence of ROA and pain was assessed in the hips of all participants. The association between hip pain and ROA was assessed using generalised estimating equations. Results The prevalence of definite ROA was 11.0% ( n = 218/1982 hips), with prevalence in painful and pain-free hips of 13.3% ( n = 105/789) and 9.5% ( n = 113/1193), respectively. Prevalence of early-stage hip ROA was 35.3% ( n = 700/1982), with prevalence in painful and pain-free hips of 41.2% ( n = 325/789) and 31.4% ( n = 375/1193), respectively. Compared with pain-free hips, the odds ratio painful hips was 1.51 (95% confidence interval [CI] = 1.16 to 1.98) for definite ROA and 1.47 (95% CI = 1.24 to 1.75) for early-stage ROA. Conclusion Hip pain was associated with definite and early-stage hip ROA, yet the overall ROA prevalence was modest and the prevalence among pain-free hips was substantial. Therefore, radiographs provided little assistance with help to identify patients with hip OA among patients who recently presented with hip or knee complaints.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".