Prevalence of and risk factors for pre-sarcopenia among healthcare professionals
Bibliographic record
Abstract
Objectives Sarcopenia, characterized by a progressive loss of skeletal muscle mass and function, constitutes a health issue that remains largely undiagnosed. Few studies focused on the presence of sarcopenia in healthcare professionals. This study examined the prevalence of and risk factors for sarcopenia among this cohort. Methods For this cross-sectional study, we recruited healthcare professionals from the National Cheng Kung University Hospital in Taiwan. Sarcopenia was defined in accordance with the European Working Group on Sarcopenia in Older People (2010) and the Asian Working Group for Sarcopenia (2019) guidelines. Skeletal muscle mass indices were measured via bioelectrical impedance analysis. Muscle strength was evaluated with a hand-grip test and physical performance was assessed based on 6-meter walk gait speed. Hormone levels were examined. Logistic regression was used to determine the prevalence of sarcopenia and its risk factors. Results One hundred participants (41.8 ± 13.3 years, 53% female) were recruited. The overall prevalence of sarcopenia was 22.0% (male/female: 4%/18%), with most cases identified as pre-sarcopenia (19.0%). Logistic regression revealed age, alcohol consumption, calf circumference, protein mass, and serum albumin concentration were associated with sarcopenia. Conclusions This is the first study to investigate sarcopenia in healthcare professionals. The findings indicate a high prevalence of pre-sarcopenia associated with higher age, smaller calf circumference, lower protein mass and serum albumin concentration. Light alcohol consumption may have a protective effect. Our results emphasize the importance of paying special attention to the impact of sarcopenia in the healthcare industry and raising awareness of its potential harm.
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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 | 0.000 |
| 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) | 0.003 | 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".