Exploring the Potential Effectiveness of Combining Optimal Nutrition With Electrical Stimulation to Maintain Muscle Health in Critical Illness: A Narrative Review
Bibliographic record
Abstract
Muscle wasting occurs rapidly within days of an admission to the intensive care unit (ICU). Concomitant muscle weakness and impaired physical functioning can ensue, with lasting effects well after hospital discharge. Early physical rehabilitation is a promising intervention to minimize muscle weakness and physical dysfunction. However, there is an often a delay in commencing active functional exercises (such as sitting on the edge of bed, standing and mobilizing) due to sedation, patient alertness, and impaired ability to cooperate in the initial days of ICU admission. Therefore, there is high interest in being able to intervene early through nonvolitional exercise strategies such as electrical muscle stimulation (EMS). Muscle health characterized as the composite of muscle quantity, as well as functional and metabolic integrity, may be potentially maintained when optimal nutrition therapy is provided in complement with early physical rehabilitation in critically ill patients; however, the type, dosage, and timing of these interventions are unclear. This article explores the potential role of nutrition and EMS in maintaining muscle health in critical illness. Within this article, we will evaluate fundamental concepts of muscle wasting and evaluate the effects of EMS, as well as the effects of nutrition therapy on muscle health and the clinical and functional outcomes in critically ill patients. We will also highlight current research gaps in order to advance the field forward in this important area.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".