The GLIM consensus approach to diagnosis of malnutrition: A 5-year update
Bibliographic record
Abstract
BACKGROUND: The Global Leadership Initiative on Malnutrition (GLIM) introduced an approach for malnutrition diagnosis in 2019 comprised of screening followed by assessment of three phenotypic criteria: weight loss, low BMI, and low muscle mass, and two etiologic criteria: reduced food intake/assimilation, and inflammation/disease burden. This planned update reconsiders the GLIM framework based on published knowledge and experience over the past five years. METHODS: A GLIM working group (n = 43 members) conducted a literature search spanning 2019-2024 using the keywords "Global Leadership Initiative on Malnutrition or GLIM". Prior GLIM activities providing guidance for use of the criteria on muscle mass and inflammation were reviewed. Successive rounds of review and revision were used to achieve consensus. RESULTS: More than 400 scientific reports are published in peer-reviewed journals, forming the basis of 10 systematic reviews, some including meta-analyses of GLIM validity that indicate strong construct and predictive validity. Limitations and future priorities are discussed. Working group findings suggest that assessment of low muscle mass should be guided by experience and available technological resources. Clinical judgement may suffice to evaluate the inflammation/disease burden etiologic criterion. No revisions of the weight loss, low BMI, or reduced food intake/assimilation criteria are suggested. Following two rounds of review and revision, the working group secured 100 % agreement with the conclusions reported in the 5-year update. CONCLUSION: Ongoing initiatives target priorities that include malnutrition risk screening procedures, GLIM adaptation to the intensive care setting, assessment in support of the reduced food intake/assimilation criterion, and determination of malnutrition in obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".