Effect of body mass index on functional outcome in primary total knee arthroplasty - a single institution analysis of 2180 primary total knee replacements
Bibliographic record
Abstract
AIM: To evaluate the effect of body mass index (BMI) on short-term functional outcome and complications in primary total knee arthroplasty. METHODS: All patients undergoing primary total knee arthroplasty at a single institution between 2007 and 2013 were identified from a prospective arthroplasty database. 2180 patients were included in the study. Age, gender, BMI, pre- and post-operative functional scores [Western Ontario and McMaster University Arthritis Index (WOMAC) and SF-36], complications and revision rate were recorded. Patients were grouped according to the WHO BMI classification. The functional outcome of the normal weight cohort (BMI < 25) was compared to the overweight and obese (BMI ≥ 25) cohort. A separate sub-group analysis was performed comparing all five WHO BMI groups; Normal weight, overweight, class 1 obese, class 2 obese and class 3 obese. RESULTS: < 0.01) There were 32 (1.47%) superficial infections, 9 (0.41%) deep infections and 19 (0.87%) revisions overall with no complications or revisions in the normal weight cohort (BMI < 25). CONCLUSION: Post-operative functional outcome was not influenced by BMI comparing normal weight individuals with BMI > 25. Patients should not be denied total knee arthroplasty based solely on weight alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".