Does Posterior Condylar Offset Affect Clinical Results following Total Knee Arthroplasty?
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is an effective, durable treatment for knee osteoarthritis. However, a subset of patients experiences incomplete pain relief and ongoing dysfunction. Posterior condylar offset (PCO) has previously been shown to be associated with postoperative range of motion (ROM) following TKA; however, an association with patient-reported outcome measures (PROMs) has not been established. The purpose of this study was to evaluate the association between PCO and postoperative ROM and PROMs. A retrospective review of 970 posterior-stabilized single design TKAs was performed. Preoperative and postoperative radiographs were analyzed to measure the change in PCO and anteroposterior (AP) femoral dimension. Clinical outcome measures, including Short Form-12 physical and mental component summaries, Western Ontario and McMaster Universities Arthritis Index, and Knee Society Score were reviewed to determine if these were influenced by changes in PCO and AP dimension. PCO was increased by more than 3 mm in 15.1%, maintained (within 3 mm) in 59.6%, and decreased by more than 3 mm in 25.3% of patients. Comparing between these groups, there were no significant differences in postoperative ROM or PROM. AP dimension increased in 24.4%, maintained in 47.8%, and decreased in 27.8%. Similarly, there were no significant differences in ROM or PROM between these groups. Spearman's correlation analyses failed to identify an association between PCO and ROM or PROMs. In conclusion, increasing or decreasing PCO or AP femoral dimension with this PS TKA design did not significantly affect postoperative ROM or PROM. Similarly, maintenance of PCO within one implant size with this system compared with optimal sizing had no deleterious effect on TKA 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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".