Does Periacetabular Osteotomy for Hip Dysplasia Modulate Cartilage Biochemistry?
Bibliographic record
Abstract
BACKGROUND: The aim of periacetabular osteotomy is to improve joint mechanics in patients with developmental dysplasia of the hip. In our study, we tried to determine whether the proteoglycan content, as measured with delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), can be modulated with the alteration of the hip joint biomechanics. METHODS: In this prospective cohort study, thirty-seven patients (thirty-seven hips) with no or minimal osteoarthritis were treated with periacetabular osteotomy for symptomatic acetabular dysplasia. All patients had preoperative and one-year follow-up dGEMRIC scans. Twenty-eight of the thirty-seven also had two-year scans. The changes in dGEMRIC findings and hip morphology between the preoperative visit and the examinations at one and two years following the periacetabular osteotomy were assessed. RESULTS: The mean preoperative dGEMRIC index (and standard deviation) was 561.6 ± 117.6 ms; this decreased to 515.2 ± 118.4 ms at one year after periacetabular osteotomy but subsequently recovered to 529.2 ± 99.1 ms at two years postoperatively. The decrease in the dGEMRIC index of the acetabular cartilage after surgery appears to be most pronounced at the superior aspect of the acetabulum, where the decrease in mechanical loading after periacetabular osteotomy would be most pronounced. All domains of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) demonstrated significant improvement from the preoperative to the postoperative visits (all p < 0.001). CONCLUSIONS: Periacetabular osteotomy for developmental dysplasia of the hip appears to alter the mechanical loading of articular cartilage in the hip, which in turn alters the cartilage matrix composition, as demonstrated by dGEMRIC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".