Muscle Mass Loss in the Older Critically Ill Population: Potential Therapeutic Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it