Determination of Nutrition Risk and Status in Critically Ill Patients: What Are Our Considerations?
Bibliographic record
Abstract
The stress catabolism state predisposes critically ill patients to a high risk of malnutrition. This, coupled with inadequate or delayed nutrition provision, will lead to further deterioration of nutrition status. Preexisting malnutrition and iatrogenic underfeeding are associated with increased risk of adverse complications. Therefore, accurate detection of patients who are malnourished and/or with high nutrition risk is important for timely and optimal nutrition intervention. Various tools have been developed for nutrition screening and assessment for hospitalized patients, but not all are studied or validated in critically ill populations. In this review article, we consider the pathophysiology of malnutrition in critical illness and the currently available literature to develop recommendations for nutrition screening and assessment. We suggest the use of the (modified) Nutrition Risk in the Critically Ill (mNUTRIC) for nutrition risk screening and the subjective global assessment (SGA) together with other criteria relevant to the critically ill patients, such as gastrointestinal function, risk of aspiration, determination of sarcopenia and frailty, and risk of refeeding syndrome for nutrition assessment. Further research is needed to identify suitable nutrition monitoring indicators to determine the response to the provision of nutrition.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".