FP2.7 Patient-Specific Predictors of Functional Recovery Following Periacetabular Osteotomy
Bibliographic record
Abstract
Abstract Background Patient-reported outcome measures (PROMs) have become essential for evaluating clinical outcomes following periacetabular osteotomy (PAO). Recent studies defined thresholds for Minimum Clinically Important Difference (MCID), Substantial Clinical Benefit (SCB), and Patient-Acceptable Symptom State (PASS) for PAO patients. This study aimed to assess these thresholds and identify predictive factors influencing their attainment. Methods We retrospectively analyzed prospective clinical and radiological data from 150 patients who underwent PAO for symptomatic hip dysplasia. MCID, SCB, and PASS thresholds were defined for the International Hip Outcome Tool-33 (iHOT-33) based on published anchor-based criteria (MCID ≥26 points improvement, SCB ≥42 points improvement, PASS absolute score ≥65). Patients were divided into groups based on achievement of these thresholds. Bivariate analyses screened predictive factors, including age, BMI, perioperative additional procedures, and postoperative radiographic parameters (LCEA, acetabular inclination, and alpha angle). A subsequent multivariate logistic regression was performed on significant factors. Results At 6, 12, and 24 months postoperatively, the proportions of patients reaching MCID were 70.0%, 71.9%, and 80.7%, respectively. SCB was achieved by 40.2%, 50.7%, and 59.7%, while PASS was reached by 66.0%, 69.1%, and 79.8% of patients at the respective time points. Bivariate analysis identified BMI, postoperative acetabular inclination, and labral refixation as relevant predictors. Multivariate regression confirmed that higher BMI significantly reduced the likelihood of achieving MCID, SCB, and PASS at 12 months (p=0.022, p=0.027, p=0.027, respectively). Greater postoperative acetabular inclination reduced the probability of reaching MCID at 12 months (p=0.022). Labral refixation positively influenced PASS at 6 months but not at later timepoints. Conclusion This study highlights the importance of postoperative radiographic parameters and patient-specific factors in predicting meaningful clinical improvement after PAO. A higher BMI significantly reduces the likelihood of improvement, particularly at 12 months. A larger postoperative acetabular inclination negatively impacts short-term functional improvement. Labral refixation benefits early symptoms but not long-term outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".