Body mass index, pain and function in individuals with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Obesity is a risk factor for progression of knee osteoarthritis (OA), and high body mass index (BMI) may interfere with treatment effectiveness on pain and function in individuals with knee OA. This study investigated the effects of BMI on pain and function during a four-week exercise programme in patients with knee OA. MATERIALS AND METHODS: Forty-six (31 women and 15 men) participants with knee OA of different BMI categories (15 normal weight participants, 13 over weight participants and 18 obese participants), received standardised exercise therapy programme twice a week for 4 weeks. Outcome included a 10-point pain rating scale for pain-intensity and the western Ontario and McMaster university osteoarthritis index (WOMAC) for physical function. RESULTS: Two-way repeated measure analysis of variance (ANOVA) on pain assessment score revealed a significant effect of time (F = 1049.401, P < 0.001) and group (F = 9.393, P < 0.001) on pain. Similar significant effect of time (F = 595.744, P < 0.001) and group (F = 5.431, P = 0.008) was obtained for WOMAC score on function. Post hoc analysis revealed significant difference between the normal weight and overweight group (t = 2.472, P = 0.016) and between normal weight and obese group (t = 3.893, P = 0.005) on pain outcome at the 4(th) week post treatment. No significant difference was found at 4(th) week post treatment on WOMAC scores (F = 2.010, P = 0.146). CONCLUSION: Exercise improved pain and function scores in OA patients across the BMI groups. Overweight independent of obesity may interfere with effectiveness of pain control during the symptomatic treatment of knee OA patients.
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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 | 0.000 | 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) | 0.002 | 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".