Body Composition, Strength, and Dietary Intake of Patients with Hip or Knee Osteoarthritis
Bibliographic record
Abstract
PURPOSE: To describe body composition (fat mass (FM) and fat-free mass (FFM)), strength, and nutritional characteristics of patients with hip or knee osteoarthritis undergoing total joint arthroplasty. METHODS: In this prospective pilot study, osteoarthritic patients underwent body composition assessment using bioelectrical impedance analysis, grip strength measurement, and completed a 24-h dietary recall during their pre-operative assessment. RESULTS: Fifty-five patients were included (∼66% females, age 43-89 years). Mean ± SD body mass index (BMI) was 32.79 ± 6.48 kg/m(2) and 62% were obese. Compared with hip osteoarthritis patients, knee osteoarthritis patients had a higher BMI (P = 0.018) and males with knee osteoarthritis had a lower grip strength (P = 0.028). There was a wide range in FM and FFM values across the BMI spectrum. Patients with a higher FM index (FMI, FM/height in m(2)) had higher levels of pain (P = 0.036) and females with higher FMI had a lower grip strength (P = 0.048). Dietary under-reporting was common and many patients did not meet recommendations for protein, vitamins C and E, or omega-3 fatty acids. Those who consumed less protein than the recommended dietary allowance were older (P = 0.018). CONCLUSIONS: A wide variability of body composition and dietary intake was observed which may impact strength and ultimately affect physical function. As such, patients with osteoarthritis may benefit from targeted nutrition and physical activity interventions before and after surgery.
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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".