Implications of low muscle mass across the continuum of care: a narrative review
Bibliographic record
Abstract
Abnormalities in body composition can occur at any body weight. Low muscle mass is a predictor of poor morbidity and mortality and occurs in several populations. This narrative review provides an overview of the importance of low muscle mass on health outcomes for patients in inpatient, outpatient and long-term care clinical settings. A one-year glimpse at publications that showcases the rapidly growing research of body composition in clinical settings is included. Low muscle mass is associated with outcomes such as higher surgical and post-operative complications, longer length of hospital stay, lower physical function, poorer quality of life and shorter survival. As such, the potential clinical benefits of preventing and reversing this condition are likely to impact patient outcomes and resource utilization/health care costs. Clinically viable tools to measure body composition are needed for routine screening and intervention. Future research studies should elucidate the effectiveness of multimodal interventions to counteract low muscle mass for optimal patient outcomes across the healthcare continuum. Key messages Low muscle mass is associated with several negative outcomes across the healthcare continuum. Techniques to identify and counteract low muscle mass in clinical settings are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".