Identifying Patients with Osteoarthritis at Risk of Sarcopenia using the SARC-F
Bibliographic record
Abstract
BACKGROUND: Sarcopenia is an important modifiable risk factor in patients being considered for elective knee or hip replacement as it may be associated with a higher risk of post-operative joint replacement complications. Our objectives are to determine the prevalence of patients with osteoarthritis at risk of sarcopenia by using the SARC-F tool, and whether risk of sarcopenia is associated with referral to an orthopaedic surgeon. METHODS: We conducted a retrospective review of patients who were 60 years or older assessed at four Canadian musculoskeletal assessment centres. Patients completed the SARC-F as part of their assessment. Multivariable logistic regression analyses were conducted to determine association between risk of sarcopenia and the odds of referral to an orthopedic surgeon for surgical consultation. RESULTS: 3,697 patients were included and 67.8% (2,508/3,697) were at risk of sarcopenia. Prevalence was highest in those assessed for hip replacement at 72.3% (635/878). Patients at risk of sarcopenia were more likely to be referred to an orthopaedic surgeon (OR 1.299; SD 1.074-1.571). CONCLUSIONS: Patients with osteoarthritis assessed for joint replacement are at high risk of sarcopenia, particularly individuals undergoing potential hip replacement. Patients at risk of sarcopenia are more likely to be referred to orthopaedic surgery for surgical consultation.
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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 | 0.002 |
| 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".