Evaluation of the Global Leadership Initiative on Malnutrition Criteria Using Different Muscle Mass Indices for Diagnosing Malnutrition and Predicting Survival in Lung Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Malnutrition is prevalent in lung cancer (LC) patients, yet there are no globally accepted criteria for diagnosing malnutrition. Recently, the Global Leadership Initiative on Malnutrition (GLIM) criteria were proposed. However, the role of these criteria in prospective LC cohorts remains unclear. METHODS: We performed a multicenter, observational cohort study including 1219 LC patients. Different anthropometric measures were compared for assessment of reduced muscle mass (RMM) in the GLIM criteria. Least absolute shrinkage and selection operator and multivariate Cox regressions were performed to analyze the association between the GLIM criteria and survival. Independent prognostic predictors were incorporated to develop a nomogram for individualized survival prediction, and decision curve was applied to assess the clinical significance of the nomogram. RESULTS: Patients in the stage II (severe) malnutrition group, diagnosed using combined calf circumference (CC) plus body weight-standardized handgrip strength (HGS/W) criteria, had the highest hazard ratio (HR, 2.07; 95%CI, 1.50-2.86) compared with other methods used to evaluate RMM. The GLIM criteria diagnosed malnutrition in 24% of cases (292 patients, using the CC and HGS/W criteria) and were effective for determining the nutrition status of LC patients. GLIM-diagnosed malnutrition was an independent risk factor for survival, and malnutrition severity was monotonically associated with death hazards (P = .002). The GLIM nomogram showed good performance in predicting the survival of LC patients, and the decision-curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION: These findings support the effectiveness of GLIM in diagnosing malnutrition and predicting survival among LC patients.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".