The Impact Of Body Mass Index On The Clinical Outcomes Of Unicondylar Knee Arthroplasty
Bibliographic record
Abstract
Objectives: The aim of this study is to investigate the effects of the body mass index on the postoperative functional knee data of patients who have undergone unicondylar knee arthroplasty with a fixed insert system.Patients and methods: Body mass index was calculated with the method proposed by the World Health Organization. The patients were divided into two groups based on their body mass index, those with a body mass index of below and above 30 kg/m2. Preoperative and postoperative joint range of motion, Visual Analogue Scale, Knee Society Scores, Oxford Knee Scores, Western Ontario and McMaster Universities osteoarthritis index scores were used to identify the patients’ satisfaction and the functional status of their knees.Results: Among the 82 patients were 44 patients (Group 1) with a body mass index below 30 kg/m2 and 38 patients (Group 2) with a body mass index above 30 kg/m2. There was no statistically significant difference between the groups in terms of their demographic data other than their body weights and body mass indexes, and their follow-up lengths (p˃0.05). A statistically significant improvement in the postoperative period was identified in both groups in the intragroup evaluation of the functional knee scores (p:0.001). However, no statistically significant difference was found in these parameters between the groups (p˃0.05). Conclusion: Body mass index reaching the level of obesity would not affect postoperative clinical results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".