Being Underweight Is Associated with Worse Surgical Outcomes of Total Knee Arthroplasty Compared to Normal Body Mass Index in Elderly Patients
Bibliographic record
Abstract
Hyuck Min Kwon, Chang Dong Han, Ick-Hwan Yang, Woo-Suk Lee, Chan Woo Kim, Kwan Kyu Park Department of Orthopedic Surgery, Yonsei University College of Medicine, Seoul, KoreaCorrespondence: Kwan Kyu ParkDepartment of Orthopedic Surgery, Yonsei University, College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, KoreaTel +82 2-2228-2180Fax +82 2-363-1139Email kkpark@yuhs.acPurpose: Being underweight has never been studied in relation to the radiologic and clinical outcomes of total knee arthroplasty (TKA) in elderly patients. The aim of this study was to determine the effect of being underweight on TKA radiological and clinical outcomes and to investigate whether being underweight influences postoperative complications compared to normal body mass index (BMI) in elderly patients.Patients and Methods: A total of 118 female patients aged 65 years or older with BMI < 25 kg/m2 who underwent primary TKA were divided into two groups based on BMI: group A: 18.5 kg/m2 < BMI < 25 kg/m2; group B: BMI < 18.5 kg/m2. The radiologic and clinical outcomes were evaluated at follow-up of 6, 12, and 24 months after surgery such as the hip-knee-ankle angle, the American Knee Society (AKS) score, Western Ontario and McMaster University score (WOMAC), and patellofemoral (PF) scale. Moreover, postoperative complications during follow-up were investigated.Results: Preoperative clinical scores did not differ significantly between the two groups. Postoperative WOMAC pain (1.8 ± 1.9 versus 3.4 ± 2.6, p = 0.02), WOMAC function (12.4 ± 8.1 versus 16.5 ± 8.5, p = 0.012) and PF scales (26.1 ± 3.6 versus 23.7 ± 4.1, p = 0.002) were worse in the underweight group at 12 and 24 months after surgery. The frequency of postoperative complications did not differ significantly between groups. In multivariate linear regression analysis, underweight patient group was significantly associated with worse postoperative WOMAC and PF scores (p = 0.002, 0.005).Conclusion: Although postoperative complications of TKA did not differ between groups, underweight patients had worse clinical outcomes of TKA compared to patients with normal BMI in elderly patients. Therefore, care should be taken when performing TKA in elderly underweight patients.Keywords: total knee arthroplasty, clinical outcome, body mass index, underweight
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".