Protein Turnover and Metabolism in the Elderly Intensive Care Unit Patient
Bibliographic record
Abstract
Many intensive care unit (ICU) patients do not achieve target protein intakes particularly in the early days following admittance. This period of iatrogenic protein undernutrition contributes to a rapid loss of lean, in particular muscle, mass in the ICU. The loss of muscle in older (aged >60 years) patients in the ICU may be particularly rapid due to a perfect storm of increased catabolic factors, including systemic inflammation, disuse, protein malnutrition, and reduced anabolic stimuli. This loss of muscle mass has marked consequences. It is likely that the older patient is already experiencing muscle loss due to sarcopenia; however, the period of stay in the ICU represents a greatly accelerated period of muscle loss. Thus, on discharge, the older ICU patient is now on a steeper downward trajectory of muscle loss, more likely to have ICU-acquired muscle weakness, and at risk of becoming sarcopenic and/or frail. One practice that has been shown to have benefit during ICU stays is early ambulation and physical therapy (PT), and it is likely that both are potent stimuli to induce a sensitivity of protein anabolism. Thus, recommendations for the older ICU patient would be provision of at least 1.2-1.5 g protein/kg usual body weight/d, regular and early utilization of ambulation (if possible) and/or PT, and follow-up rehabilitation for the older discharged ICU patient that includes rehabilitation, physical activity, and higher habitual dietary protein to change the trajectory of ICU-mediated muscle mass loss and weakness.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".