The effects of obesity on functional outcomes after total knee arthroplasty: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: An increasing number of total knee arthroplasties (TKAs) are performed in people with obesity, but TKAs in this population may come with increased risk of perioperative complications and decreased prosthetic survivorship. Given the lack of conclusive evidence on differences in functional outcomes, we aimed to use the Forgotten Joint Score-12 (FJS-12) to see how body mass index (BMI) affected functional outcomes after TKA. METHODS: We recruited patients who underwent primary unilateral TKA because of osteoarthritic changes from January 2018 to November 2021. We collected the Forgotten Joint Score-12 (FJS-12) measure of functional outcomes and the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) preoperatively and 6- and 12-months postoperatively. We also measured length of stay (LOS), readmission, and emergency department (ED) visits. We compared outcomes by BMI category using linear effects models. RESULTS: = 0.02) scores. At 6 months, patients with lower BMI showed a greater change in FJS-12 scores than those with higher BMI. However, by 12 months, all patients appeared to return to similar functional levels regardless of BMI. CONCLUSION: Despite a slower return to function, patients with elevated BMI were able to return to similar levels of function as those with a lower BMI by 12 months, with no significant differences in readmission, ED visits, or LOS. This similar return to function justifies candidacy for surgery.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".