Relationship Between Hip Dysplasia, Pain, and Osteoarthritis in a Cohort of Patients with Hip Symptoms
Bibliographic record
Abstract
OBJECTIVE: The relationship between acetabular dysplasia (HD) and hip osteoarthritis (OA) remains unclear, especially for mild forms of dysplasia. Our objectives were to estimate the prevalence of HD in a population-based sample with symptoms and to evaluate potential associations linking HD, hip OA, and hip pain. METHODS: Individuals 40 to 75 years of age with symptoms in 1 or both hips were recruited during a multiregional prevalence survey. All study participants underwent examination and radiographs. Radiographs were evaluated using Kellgren-Lawrence staging (with stages ≥ 2 indicating hip OA) and HD measures [center-edge (CE) angle, acetabular inclination angle (HTE), acetabular depth (AD), and vertical center-anterior margin angle]. RESULTS: We studied both hips of 842 individuals (1684 hips), among whom 203 had hip OA. Compared to left hips, right hips had significantly smaller CE angles and significantly greater AD and HTE values (p ≤ 0.001). Overall, the prevalence of HD ranged from 7.6% to 22.2% of the hips depending on the measure used. The prevalence of HD was higher in individuals with hip OA, with significant differences for abnormal HTE (19.1% vs 11.4%; p < 0.0001) and abnormal CE (11.3% vs 7.5%; p = 0.04). By logistic regression, only abnormal HTE remained associated with OA. Same-side hip pain was not statistically more common in individuals with HD after stratification on OA status (p = 0.12). CONCLUSION: Our study confirms the relationship between OA and HD, particularly as defined based on the HTE angle.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".