GLIM Criteria for the Diagnosis of Malnutrition: A Consensus Report From the Global Clinical Nutrition Community
Bibliographic record
Abstract
BACKGROUND: This initiative aims to build a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings. METHODS: The Global Leadership Initiative on Malnutrition (GLIM) was convened by several of the major global clinical nutrition societies. Empirical consensus was reached through a series of face-to-face meetings, telephone conferences, and e-mail communications. RESULTS: A 2-step approach for the malnutrition diagnosis was selected, that is, first screening to identify at risk status by the use of any validated screening tool, and second, assessment for diagnosis and grading the severity of malnutrition. The malnutrition criteria for consideration were retrieved from existing approaches for screening and assessment. Potential criteria were subjected to a ballot among GLIM participants that selected 3 phenotypic criteria (non-volitional weight loss, low body mass index, and reduced muscle mass) and 2 etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden). To diagnose malnutrition at least 1 phenotypic criterion and 1 etiologic criterion should be present. Phenotypic metrics for grading severity are proposed. It is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes. The recommended approach supports classification of malnutrition into four etiology-related diagnosis categories. CONCLUSIONS: A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed. Next steps are to secure endorsements from leading nutrition professional societies, to identify overlaps with syndromes like cachexia and sarcopenia, and to promote dissemination, validation studies, and feedback. The construct should be re-considered every 3-5 years.
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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 | 0.073 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".