Malnutrition defined by GLIM criteria identifies a higher incidence of malnutrition and is associated with pulmonary complications after oesophagogastric cancer surgery, compared to ICD‐10‐defined malnutrition
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Low muscle mass, measured using computed tomography (CT), is associated with poor surgical outcomes. We aimed to include CT-muscle mass in malnutrition diagnosis using the Global Leadership Initiative on Malnutrition (GLIM) criteria, compare it to the International Classification of Diseases 10th Revision (ICD-10) criteria, and assess the impact on postoperative outcomes after oesophagogastric (OG) cancer surgery. METHODS: One hundred and eight patients who underwent radical OG cancer surgery and had preoperative abdominal CT imaging were included. GLIM and ICD-10 malnutrition data were assessed against complication and survival outcomes. Low CT-muscle mass was determined using predefined cut-points. RESULTS: GLIM-defined malnutrition prevalence was significantly higher than ICD-10-malnutrition (72.2% vs. 40.7%, p < 0.001). Of the 78 patients with GLIM-defined malnutrition, low muscle mass (84.6%) was the predominant phenotypic criterion. GLIM-defined malnutrition was associated with pneumonia (26.9% vs. 6.7%, p = 0.010) and pleural effusions (12.8% vs. 0%, p = 0.029). Postoperative complications did not correlate with ICD-10 malnutrition. Severe GLIM (HR: 2.51, p = 0.014) and ICD-10 (HR: 2.15, p = 0.039) malnutrition were independently associated with poorer 5-year survival. CONCLUSIONS: GLIM criteria appear to identify more malnourished patients and more closely relate to surgical risk than ICD-10 malnutrition, likely due to incorporating objective muscle mass assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".