Correlates and predictors of sarcopenia among men with metastatic castrate-resistant prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Sarcopenia is a predictor of clinical outcomes in men with metastatic castrate-resistant prostate cancer (mCRPC); however, correlates and predictors of sarcopenia are poorly understood in this population. The aim of this study was to examine correlates and predictors of sarcopenia in men with mCRPC prior to treatment. METHODS: A secondary analysis of an observational study was performed. Participants were receiving care for mCRPC at the Princess Margaret Cancer Centre. Sarcopenia was assessed prior to treatment and was defined as the combination of low grip strength (<35.5 kg), low gait speed (<0.8 m/s), and computed tomography-derived low muscle mass or density. Participants' sociodemographic and clinical characteristics, comorbidity information, and clinically relevant blood markers were collected prior to treatment and were used to identify correlates and predictors of sarcopenia through Spearman correlations and multivariable logistic regression, respectively. RESULTS: In total, 110 men had complete data on sarcopenia measures and were included in the analysis. Sarcopenia was identified in 30 (27.3%) participants. Pre-treatment sarcopenia was moderately correlated with dependence in one or more instrumental activities of daily living (IADLs) (r=0.412), Vulnerable Elders Survey-13 (r=0.404), and a lower hemoglobin (r=0.407 per 10 g/L decrease). In adjusted logistic regression, dependence in one or more IADLs (odds ratio [OR] 4.37, 95% confidence interval [CI] 1.37-13.86, p=0.012), and a 10 g/L decrease in hemoglobin (OR 1.70, 95% CI 1.13-2.57, p=0.012) were significantly associated with sarcopenia. CONCLUSIONS: In settings where assessment of sarcopenia is not feasible, evaluation of IADLs and hemoglobin may be used to identify high-risk patients that can benefit from supportive care strategies aiming to improve muscle mass and function.
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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".