Usefulness of the global leadership initiative on malnutrition -GLIM- criteria to identify malnutrition in older adults: systematic review and meta-analyses
Bibliographic record
Abstract
AIM: Malnutrition is highly prevalent among older subjects, yet no universal method for its identification has been established. This study aimed to evaluate the concurrent and predictive validity of the GLIM criteria in older adults. METHODS: A systematic review and meta-analysis were conducted in five databases involving older adults (≥60 years). Concurrent validity utilizes reference nutritional assessment tools, while predictive validity focuses on key outcomes, including mortality, hospitalization, and healthcare costs. RESULTS: The search yielded 683 references. Thirteen hospital-based studies from four continents (n = 9164) met the inclusion criteria. GLIM implementation commonly used the MNA-SF for screening, while muscle mass was assessed with calf circumference, BIA, or DXA. The global prevalence of malnutrition, as reported by GLIM, ranged from 14.1% to 71.9%. A meta-analysis of four studies (n = 1221) showed that malnutrition diagnosis by GLIM criteria demonstrated a pooled sensitivity of 0.79 (95% CI: 0.69-0.86) and specificity of 0.88 (95% CI: 0.81-0.93), with moderate heterogeneity and no publication bias. Predictive validity analyses showed that malnutrition diagnosed by GLIM was associated with higher in-hospital and post-discharge mortality, lower ADL scores at discharge and follow-up, and longer hospital stays. Malnutrition was also associated with an increased risk of hospital complications and higher healthcare costs. No validation studies were found in nursing homes or community-dwelling older adults. CONCLUSION: The GLIM criteria appear suitable for identifying malnutrition in clinical settings and predicting key adverse outcomes. However, their application in community-dwelling and nursing home populations remains inconclusive until further evidence becomes available.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".