Toward Precision in Nutrition Therapy
Bibliographic record
Abstract
Precision in critical care nutrition is paramount, as it focuses nutrition interventions on those patients most likely to benefit, or those who might potentially be harmed. Critical care nutrition must therefore be tailored to individual metabolic needs as determined by factors that control the capacity for tissue homeostasis and anabolic responses. This ideally involves the accurate and timely assessment of macronutrient and micronutrient requirements, a careful evaluation of metabolic response mechanisms and the identification of circumstances that might interfere with the productive utilization of dietary substrates. Specific surrogate markers of metabolic response, such as blood glucose levels, urea levels, or nitrogen balance, might be used to evaluate the metabolic readiness for nutrition and to establish the timing, nature, and clinical effectiveness of nutrition interventions. Despite the pressing need to further develop more targeted approaches in critically ill patients, indices of immediate metabolic responses that correlate with favorable clinical outcomes are lacking. In addition, the development of precision approaches might address timely adjustments in protein, energy, or micronutrient supplementation based on evolving clinical conditions. Here, we review why precision tools are needed in critical care nutrition, our progress thus far, as well as promising approaches and technologies by which multidisciplinary healthcare teams can improve quality of care and clinical outcomes by individualizing nutrition interventions.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".