The Optimal Variation Range of Posterior Condylar Offset Associated with Positive Clinical Outcomes of Primary Total Knee Arthroplasty: A Retrospective Analysis
Bibliographic record
Abstract
OBJECTIVE: Posterior condylar offset (PCO) and anterior condylar offset (ACO) exert an influence on the sagittal alignment in total knee arthroplasty (TKA). However, there is no common consensus that the variation range of posterior condylar offset (PCO) is associated with patient-reported outcome measures (PROMs) and the optimum variation range of PCO. This study aims to investigate the correlation between PCO and the PROMs of primary TKA for osteoarthritis (OA) and find out the optimal variation range of the PCO. METHODS: In this study, we performed a radiographic analysis of 106 patients (112 knees) with primary TKA. Patients were divided into two cohorts (A and B) according to the Western Ontario and McMaster Universities Osteoarthritis index (WOMAC). Correlations between the sagittal parameter and WOMAC were investigated using univariate and multivariate analysis. The receiver operating characteristic (ROC) curve was used to establish the cut-off value for the optimal variation range. We then further investigated how different variation range affects the WOMAC subscale score and forgotten-joint-score-12 (FJS-12). RESULTS: Univariate analysis revealed a correlation between the variation range of PCO (p < 0.01), ACO (p < 0.01) and PROMs. Multivariate analysis showed that only PCO was associated with PROMs. In the ROC graph, the cut-off value of the variation range of PCO is 2.85 mm (AUC = 0.66, Youden index = 0.26). The WOMAC functional ability score of the group outside the PCO variation range of 2.85 mm significantly increased compared to the group within the range. CONCLUSION: In this study, PCO variation was significantly associated with clinical outcomes in TKA and the optimal PCO variation range was within 2.85 mm. Maintaining the PCO variation within 2.85 mm could enhance functional recovery and patient satisfaction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".