The influence of body mass index on the outcomes of primary total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: The body mass index (BMI) is widely recognized as a prognostic factor in multiple operations; however, the relationship between the BMI and outcomes following total knee arthroplasty (TKA) is extensively debated. We aimed to evaluate the effect of the BMI at different cutoff values on the outcomes following primary TKA. METHODS: Electronic databases (PubMed/Medline, CENTRAL, Embase and Web of Science) were systematically searched for studies investigating the association between the BMI and outcomes following primary TKA. Two investigators independently reviewed studies for eligibility, assessed the study quality using the Newcastle-Ottawa Scale and extracted the data. A meta-analysis was performed using Review Manager software. RESULTS: Twenty-eight articles including a total of 20,988 TKAs were identified. The postoperative Knee Society Score appeared to trend lower in obese (BMI ≥ 30 kg/m(2)) patients than in non-obese (BMI < 30 kg/m(2)) patients. The meta-analysis showed that revision with follow-up ≥5 years, any infection, superficial infection and deep vein thrombosis occurred statistically more frequently in obese patients, whereas a deep infection occurred statistically more frequently in morbidly obese (BMI ≥ 40 kg/m(2)) patients than in non-obese patients. No differences in aseptic loosening with follow-up ≥5 years, pulmonary embolism and perioperative mortality rates were found between obese and non-obese patients. CONCLUSIONS: Patients with a BMI ≥ 30 kg/m(2) are at a higher risk of lower functional scores and developing complications following primary TKA. It appears reasonable to encourage obese patients to lose weight before selective TKA. LEVEL OF EVIDENCE: Prognostic study, Level III.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".