Utilization and validation of the Global Leadership Initiative on Malnutrition (GLIM): A scoping review
Bibliographic record
Abstract
BACKGROUND & AIMS: The diagnosis of malnutrition remains a significant challenge despite various published diagnostic criteria. In 2018, the Global Leadership Initiative on Malnutrition (GLIM) published a set of evidence-based criteria as a framework for malnutrition diagnosis in adults. A scoping review was conducted to understand how the GLIM criteria have been used in published literature and compare the reported validation methods to published validation guidance. METHODS: Dialog and Dimensions databases were searched by publication date (January 1, 2019, through January 29, 2021). Data were extracted and mapped to the research objectives. RESULTS: Seventy-nine studies were reviewed; 32% were in patients at least 65 years of age; 67% occurred in hospitals. The majority were cohort studies (61%). Fifty-seven percent employed all 5 GLIM criteria. Regarding phenotypic criteria, 92% used low BMI, and 45% applied anthropometry as a marker for muscle mass, of which 54% used calf circumference. Regarding etiologic criteria, 72% used reduced food intake/assimilation, and 85% applied inflammation/disease burden. Validation of GLIM criteria was described in 77% of publications. CONCLUSIONS: The GLIM criteria have been studied extensively since their publication. Low BMI was the phenotypic criterion used most often, whereas both reduced food intake/assimilation and inflammation/disease burden were frequently employed as the etiologic criteria. However, how the criteria were combined and how validation was conducted were not clear in most studies. Adequately powered, methodologically sound validation studies using the complete GLIM criteria are needed in various patient populations and disease settings to assess validity for the diagnosis of malnutrition.
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 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 | 0.118 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.040 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".