The Relationship Between Sarcopenic Obesity and Knee Osteoarthritis: The SARCOB Study.
Bibliographic record
Abstract
BACKGROUND: To investigate whether sarcopenic obesity may contribute to knee osteoarthritis or not. METHODS: In this study, we assessed 140 community-dwelling adult patients. Their demographic data were recorded along with comorbidities. Anterior mid-thigh muscle thickness in the axial plane was measured on the dominant leg using ultrasound midway between the anterior superior iliac spine and the upper end of patella in millimeter. Then, the sonographic thigh adjustment ratio was calcu- lated by dividing this thickness by body mass index. ISarcoPRM algorithm was used for the diagnosis of sarcopenia. Kellgren-Lawrence grading was used for knee osteoarthritis . Functional evaluation was performed using chair stand test, gait speed, and grip strength. RESULTS: There were 50 patients with knee osteoarthritis and 90 age- and gender-similar control sub- jects. When compared with controls, anterior thigh muscle thickness, gait speed, and grip strength were found to be similar between the groups, whereas body mass index and chair stand test val- ues were higher in the knee osteoarthritis group (both P < .05). In addition, sarcopenic obesity was observed in 12 (13.3%) of control subjects and in 14 (28%) of osteoarthritis patients. When age, gen- der, exercise, smoking, and body composition type (i.e., nonsarcopenic nonobese, sarcopenic only, obese only, and sarcopenic obesity) were taken into binary logistic regression analyses, only sarcope- nic obesity [relative risk ratio = 2.705 (95% CI: 1.079-6.779)] was independently related with the knee osteoarthritis (P < .05). CONCLUSION: Our preliminary study has shown that neither sarcopenia nor obesity but sarcopenic obe- sity seems to be independently related to the knee osteoarthritis. Further longitudinal studies with larger samples are required for investigating the effects of obesity and sarcopenia on the develop- ment of knee osteoarthritis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".