Multivariate Quantitative Outcomes of Periacetabular Osteotomy Using Discrete Element Analysis
Bibliographic record
Abstract
Introduction: Multiple biomechanical models have been suggested to quantify lower limb joint contact stress distributions, with varying results. Among others, the choice of cartilage morphology and gait loading patterns can significantly affect simulation results. Moreover, there is currently no consensus on simulating the input and output data needed to obtain reliable results and enable a comprehensive analysis. Objectives: The aim of this study was to compare the reliability and clinical relevance of joint contact metrics by calculating pre‐ and postoperative hip joint contact stress distributions of a dysplastic cohort under various simulation scenarios. Methods: A cohort of 22 dysplastic patients has been treated using periacetabular osteotomy (6‐month follow‐up). Five radiographic measurements of the acetabular cup were taken from imagery pre‐ and postoperatively. Eight osteoarthritis‐predictive joint stress metrics were computed using discrete element analysis in 6 unique simulation scenarios (2 cartilage models; 3 hip gait loading profiles) pre‐ and postoperatively. Results: A multivariate analysis of variance confirmed the significant effects of treatment, cartilage model, and loading profile on the computed stress metrics ( p < 0.01). Also, average‐ and threshold‐based metrics, such as average contact area, average stress, and Maxian overdose, were shown as more reliable indicators of successful surgical treatment than the maximum‐based metrics. Finally, correlations between radiographic measurements and stress metrics revealed greater influence of the acetabular index and anterior center‐edge angle than the lateral center‐edge angle. Conclusions: Average and threshold‐based metrics, as well as the acetabular index and anterior center‐edge angle, should be of greater interest in future studies regarding hip dysplasia. Clinical Significance: Level 2 (Prospective Study: Therapeutic).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".