Effect of body mass index on postoperative mechanical alignment and long-term outcomes after total knee arthroplasty: a retrospective cohort study of 671 knees
Bibliographic record
Abstract
Background: A high body mass index (BMI) is associated with increased rates of complications after total knee arthroplasty (TKA). However, no study has examined the effect of BMI on lower limb alignment using the World Health Organization's (WHO) BMI classification. We believe that the WHO's BMI classification allows a uniform standard worldwide. We sought to investigate the potential association between a high BMI and the incidence of postoperative misalignment. We also evaluated whether a higher BMI is associated with worse clinical function. Methods: ). Both weight and height were measured by nurses on admission. Patients' preoperative HKA, gender, age, and side of surgery were collected as baseline. All the patients underwent standing, weight-bearing, full-length radiography before and after surgery to measure the mechanical hip-knee-ankle angle (HKA). We followed up patients by telephone. Among the BMI subgroups, we compared the knee function scores, including the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, Knee Society-Knee Score (KS-KS), Knee Society-Function Score (KS-FS), Forgotten Joint Score (FJS), and range of motion (ROM). A multivariate linear regression analysis and a logistic regression was conducted to examine the outcomes. Results: The study had a mean follow-up period of 8.16 years. The multivariate and logistic regression analyses revealed that preoperative alignment (P=0.002) and a higher BMI (P=0.015) were associated with a higher risk of postoperative misalignment. The WOMAC scores were higher in the normal and overweight groups than the other groups (P=0.022). The FJS and KS-KS gradually decreased as BMI increased. Conclusions: A higher BMI is associated with a greater risk of misalignment and worse long-term clinical outcome after TKA. When treating patients with high BMI, we should pay more attention to the adjustment of lower limb alignment intraoperatively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".