Hip dysplasia as risk factor for clinically relevant and radiographic hip osteoarthritis: 10-year results from the CHECK cohort
Bibliographic record
Abstract
OBJECTIVES: To investigate hip dysplasia as a risk factor for clinically relevant and incident radiographic hip OA. METHODS: From a prospective cohort (CHECK) of 1002 middle-aged, new consulters for hip and/or knee pain, 468 hips (251 individuals) were selected based on hip pain, available lateral center edge angle (LCEA) and absence of definite radiographic hip OA (Kellgren and Lawrence [KL] grade <2) at baseline, as well as available follow-up measures. Clinically relevant hip OA was defined by an expert diagnosis based on clinical and radiographic data obtained between years 5 and 10 from baseline. Incident radiographic hip OA was defined by KL grade ≥2 or a total hip replacement at the 10-year follow-up. Associations between hip dysplasia (LCEA ≤20°) and outcomes were expressed as an odds ratio (OR) adjusted for age, sex and BMI. RESULTS: At baseline, participants had a mean age of 55.5 (5.4) years, 88% were female and, on hip level, the prevalence of hip dysplasia was 3.6% (n = 17). After 10 years, hip dysplasia was associated with an increased risk for clinically relevant hip OA (OR 2.80; 95% CI: 1.15, 6.79), but not for incident radiographic hip OA (OR 0.78; 95% CI: 0.26, 2.30). CONCLUSION: In the long term, baseline hip dysplasia was associated with an increased risk for clinically relevant hip OA, but not for incident radiographic hip OA. With this in mind, we suggest that future research investigating the link between hip dysplasia and OA strives to include a definition for OA that is clinically relevant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".