Recognizing malnutrition in adults with critical illness: Guidance statements from the Global Leadership Initiative on Malnutrition
Bibliographic record
Abstract
BACKGROUND: Patients with critical illness may present with disease-related malnutrition upon intensive care unit (ICU) admission. They are at risk of development and progression of malnutrition over the disease trajectory because of inflammation, dysregulated metabolism, and challenges with feeding. METHODS: The Global Leadership Initiative on Malnutrition (GLIM) convened a panel of 36 clinical nutrition experts to develop consensus-based guidance statements addressing the diagnosis of malnutrition during critical illness using a modified Delphi approach with a requirement of ≥75% agreement. RESULTS: (1) To identify pre-existing malnutrition, we suggest evaluation within 48 h of ICU admission when feasible (100% agreement) or within 4 days (94% agreement). (2) To identify the development and progression of malnutrition, we suggest re-evaluation of all patients every 7-10 days (97% agreement). (3) To identify progressive loss of muscle mass, we suggest evaluation of muscle mass as soon as feasible (92% agreement) and again after 7-10 days (89% agreement). (4) To identify the development and progression of malnutrition before and after ICU discharge, we suggest re-evaluating nutrition status before ICU discharge and during clinical visits that follow (100% agreement). CONCLUSION: Research using consistent etiologic and phenotypic variables offers great potential to assess the efficacy of nutrition interventions for critically ill patients with malnutrition. Assessment of these variables during and beyond the ICU stay will clarify the trajectory of malnutrition and enable exploration of impactful treatment modalities at each juncture. GLIM offers a diagnostic approach that can be used to identify malnutrition in critically ill patients.
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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 | 0.063 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| 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".