Does obesity affect patient-reported outcomes following total knee arthroplasty?
Bibliographic record
Abstract
Abstract Background There is an existing perception that obesity has a negative impact on complications following total knee arthroplasty (TKA). However, data on the impact of obesity levels on patient-reported outcomes (PROMs) is sparse. We investigated the association between different obesity classes with PROMs among patients who underwent TKA. Methods We performed retrospective secondary analyses on data extracted from the total joint replacement data repository (Alberta, Canada) managed by the Alberta Bone and Joint Health Institute (ABJHI). Patients had WOMAC and EQ5D scores measured at baseline in addition to 3 and/or 12 months following TKA. Patients were stratified according to the World Health Organization (WHO) classification, into five body mass index (BMI) groups of normal, overweight, BMI class I, BMI class II, and BMI class III. The association between BMI and mean changes in WOMAC subscales (pain, function, and stiffness) and EQ-5D-5L index over the time intervals of baseline to 3 months and 3 to 12 months following TKA was assessed. Linear mixed-effects models were used, and the models were adjusted for age, sex, length of surgery, comorbidities, year of surgery, and geographical zone where the surgery was performed. Results Mean age was 65.5 years (SD = 8.7). Postoperatively, there was a significant improvement (p
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".