Association between Obesity and Short-Term Patient-Reported Outcomes following Total Knee Arthroplasty: A Retrospective Cohort Study in Japan
Bibliographic record
Abstract
Background: This study investigated the association between obesity and short-term patient-reported outcomes after total knee arthroplasty (TKA). Methods: The primary outcomes were the Western Ontario and McMaster Universities Osteoarthritis Index’s (WOMAC) pain and function scores. Data were collected preoperatively and 2 and 4 weeks after surgery. Patients were stratified into three groups based on body mass index (BMI): normal weight (BMI < 24.99 kg/m2), overweight (25 ≤ BMI < 29.99 kg/m2), and obese (BMI ≥ 30 kg/m2). The associations between BMI and the WOMAC pain and function scores were assessed using generalized linear mixed models. Results: Among the 102 patients (median age: 75.0, women [85.3%]), 29.4%, 48.0%, and 22.5% were normal weight, overweight, and obese, respectively. The mean pain and function scores at baseline were similar across the BMI-stratified groups (p = 0.727 and 0.277, respectively). The pain score significantly improved 2 weeks post-surgery (p = 0.001). The function score improved significantly 4 weeks post-surgery (p < 0.001). The group and group-by-time interaction effects lacked statistical significance. Conclusions: All patients statistically and clinically showed relevant pain reduction and functional improvement shortly after TKA, irrespective of their obesity status. These data may help healthcare professionals discuss the expectations of pain amelioration and functional improvement with TKA candidates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".