Five-Year Impact of Weight Loss on Knee Pain and Quality of Life in Obese Patients
Bibliographic record
Abstract
BACKGROUND Studies on patients with obesity who lose a considerable amount of body fat show that the severity of knee pain and movement limitation is decreased. This study aimed to analyze the effects of weight loss on knee pain and quality of life in patients with obesity. MATERIAL AND METHODS The study included patients aged 18-65 years with a body mass index (BMI) of 30 kg/m² and above, who expressed knee pain in daily life routines and applied to the Obesity Center of Adana City Training and Research Hospital as of June 2018. The retrospective analysis included age, sex, weight, height, annual radiological imaging follow-up scores (Kellgren-Lawrence), visual analog scale (VAS) scores, EuroQol-5D (EQ-5D) scores, and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores of the patients throughout the 5-year follow-up period. RESULTS The mean age of the 89 patients was 50.3±10.5 years, and 82% were women. The initial BMI, EQ-5D, VAS, and WOMAC scores differed significantly from the scores at year 5 (P=0.0001). Receiver operating characteristic analysis showed the probability of reducing the progression of knee joint degeneration was 74% if the BMI reduction was greater than 13.3% over the 5-year follow-up period. CONCLUSIONS The overall interpretation of the results was that a 13.3% or greater reduction in BMI in the first year, despite an increase in the following years, triggered improvements in various aspects of pain and functionality scores, improved quality of life, and reduced KOA progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".