Prospective validation of the Global Leadership Initiative on Malnutrition criteria for identifying malnutrition in hospitals: A protocol and feasibility pilot study
Bibliographic record
Abstract
Abstract Background The aim of this study was to pilot a protocol for prospective validation of the Global Leadership Initiative on Malnutrition (GLIM) criteria in hospital patients and evaluate its feasibility and patient acceptability. Methods The validation protocol follows the GLIM consortium's rigorous methodological guidance. Protocol feasibility was assessed against criteria on recruitment (≥50%) and data collection completion (≥80%); protocol acceptability was assessed via patient satisfaction surveys and interviews. Adult inpatients in a tertiary hospital underwent four nutrition assessments (each by a different assessor); two Subjective Global Assessments (SGAs) and two GLIM assessments. All five GLIM criteria were assessed with bioelectrical impedance analysis used for muscle mass. Interrater reliability, criterion validity, and predictive validity were reported to detect trends. Results All primary feasibility criteria were met (consent rate 76%; data for GLIM criterion validity collected on 83% participants). Of predictive outcome data, 100% of hospital‐related data, 82% of 6‐month mortality data, and 39% of 6‐month health‐related quality of life data were collected. The mean (SD) age of participants was 61.0 ± 16.2 years, and 51.5% were male. The median (interquartile range) length of stay and body mass index were 7 (4–15) days and 25.6 (24.2–33.0) kg/m2, respectively. GLIM criteria diagnosed 70% of the patients as malnourished vs 55% with SGA. Most patients found the data collection acceptable with minimal burden. Conclusion The methods outlined in this rigorous GLIM validation protocol are feasible to undertake in hospitals and acceptable to patients. This paper provides practical methodological guidance for future prospective GLIM validation studies.
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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 | 0.301 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".