Prevalence of sarcopenia indicators and sub-optimal protein intake among elective total joint replacement patients
Bibliographic record
Abstract
Sarcopenia is associated with falls, and can complicate recovery following total joint replacement (TJR) surgery. We examined (1) the prevalence of sarcopenia indicators and lower-than-recommended protein intake among TJR patients and non-TJR community participants and (2) the relationships between dietary protein intake and sarcopenia indicators. We recruited adults ≥65 years of age who were undergoing TJR, and adults from the community not undergoing TJR (controls). We assessed grip strength and appendicular lean soft-tissue mass (ALSTMBMI) using DXA, and applied the original Foundation for the National Institutes of Health Sarcopenia Project cut-points for sarcopenia indicators (grip strength <26 kg for men and <16 kg for women; ALSTM <0.789 m2 for men and <0.512 m2 for women) and less conservative cut-points (grip strength <31.83 kg for men and <19.99 kg for women; ALSTM <0.725 m2 for men and <0.591 m2 for women). Total daily and per meal protein intakes were derived from 5-day diet records. Sixty-seven participants (30 TJR, 37 controls) were enrolled. Using less conservative cut-points for sarcopenia, more control participants were weak compared with TJR participants (46% versus 23%, p = 0.055), and more TJR participants had low ALSTMBMI (40% versus 13%, p = 0.013). Approximately 70% of controls and 76% of TJR participants consumed <1.2 g protein/kg/day ( p = 0.559). Total daily dietary protein intake was positively associated with grip strength ( r = 0.44, p = 0.001) and ALSTMBMI ( r = 0.29, p = 0.03). Using less conservative cut-points, low ALSTMBMI, but not weakness, was more common in TJR patients. Both groups may benefit from a dietary intervention to increase protein intake, which may improve surgical outcomes in TJR 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 | 0.001 | 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".