Muscle Mass Loss in the Older Critically Ill Population: Potential Therapeutic Strategies
Bibliographic record
Abstract
Skeletal muscle plays a critical role in everyday life, and its age-associated reduction has severe health consequences. The pre-existing presence of sarcopenia, combined with anabolic resistance, protein undernutrition, and the pro-catabolic/anti-anabolic milieu induced by aging and exacerbated in critical care, may accelerate the rate at which skeletal muscle is lost in patients with critical illness. Advancements in intensive care unit (ICU)-care provision have drastically improved survival rates; therefore, attention can be redirected toward other significant issues affecting ICU patients (e.g., length of stay, days on ventilation, nosocomial disease development, etc.). Thus, strategies targeting muscle mass and function losses within an ICU setting are essential to improve patient-related outcomes. Notably, loading exercise and protein provision are the most compelling. Many older ICU patients seldom meet the recommended protein intake, and loading exercise is difficult to conduct in the ICU. Nevertheless, the incorporation of physical therapy (PT), neuromuscular electrical stimulation, and early mobilization strategies may be beneficial. Furthermore, a number of nutrition practices within the ICU have been shown to improve patient-related outcomes ((e.g., feeding strategy [i.e., oral, early enteral, or parenteral]), be hypocaloric (∼70%-80% energy requirements), and increase protein provision (∼1.2-2.5 g/kg/d)). The aim of this brief review is to discuss the dysregulation of muscle mass maintenance in an older ICU population and highlight the potential benefits of strategic nutrition practice, specifically protein, and PT within the ICU. Finally, we provide some general guidelines that may serve to counteract muscle mass loss in patients with critical illness.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".