Effect of posterior condylar offset on clinical results after posterior-stabilized total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: To determine the effect of the posterior condylar offset (PCO) on clinical results after total knee arthroplasty (TKA) using a high-flex posterior-stabilized (PS) fixed-bearing prosthesis. METHODS: We prospectively studied the clinical and radiographic materials of 89 consecutive female patients (89 knees), who had undergone primary TKAs for end-stage osteoarthritis. All operations were performed by a single senior surgeon or under his supervision using the same operative technique. Based on the corrected PCO change, we divided all cases into two groups: group A (corrected PCO change ≥0 mm, 58 knees) and group B (corrected PCO change<0 mm, 31 knees). One-year postoperatively, clinical and radiographic variables from the two groups were compared by independent t-test. The associations between the corrected PCO changes and the improvements of clinical variables in all patients were analyzed by Pearson linear correlation. RESULTS: One-year postoperatively, the Knee Society Scores, the Western Ontario and McMaster Universities Osteoarthritis Index, non-weight-bearing active and passive range of knee flexion, flexion contracture, extensor lag, and their improvements had no statistical differences between the two groups (all p>0.05). The corrected PCO change was not significantly correlated with the improvement of any clinical variable (all p>0.05). Group A demonstrated greater flexion than group B during active weight bearing (p<0.05). CONCLUSIONS: Restoration of PCO plays an important role in the optimization of active knee flexion during weight-bearing conditions after posterior-stabilized TKA, while it has no benefit to non-weight-bearing knee flexion or any other clinical result.
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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 | 0.003 |
| 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".