Applicability and predictive validity of the global leadership initiative on malnutrition criteria for older patients with sepsis according to different muscle mass assessment methods
Bibliographic record
Abstract
OBJECTIVES: To evaluate the applicability of the Global Leadership Initiative on Malnutrition (GLIM) criteria in older patients with sepsis and to compare the predictive validity for 28-day mortality of different muscle mass assessment methods in the emergency department. DESIGN: Prospective cohort study. SETTING: Emergency department. PATIENTS: Older patients (≥65 years) with sepsis. MEASUREMENTS: Muscle mass was assessed using three methods: (1) the skeletal muscle index at the third lumbar vertebra (L3) on computed tomography (CT) scans; (2) calf circumference (CC), and (3) mid-upper-arm circumference (MAC). Cox regression analysis was performed to assess the association between the GLIM criteria and 28-day all-cause mortality. Additionally, the C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the predictive validity of the three instruments. Survival curves were assessed using the Kaplan-Meier method and compared using the log-rank test. RESULTS: A total of 598 patients with sepsis were included. The prevalence of malnutrition according to GLIM-CT, GLIM-CC, and GLIM-MAC was 53.3%, 63.0%, and 40.8%, respectively. Cox regression analysis revealed that the GLIM criteria were independent risk factors for all-cause 28-day mortality. Incorporation of GLIM-CT, GLIM-CC, or GLIM-MAC into a base model significantly improved the C-statistic. The model including GLIM-CT had the highest C-statistic, improving the C-statistic of the base model from 0.780 (95% confidence interval [CI]: 0.741-0.819) to 0.823 (95% CI: 0.789-0.857). This improvement in risk prediction was also confirmed via category-free NRI and IDI, suggesting that GLIM-CT had the best performance. Kaplan-Meier survival analysis showed that patients with malnutrition defined according to the GLIM criteria had a greater probability of 28-day mortality (log-rank, P < 0.001). CONCLUSION: Malnutrition, defined via any of the three methods, was predictive of 28-day mortality among older patients with sepsis in the emergency department. GLIM-CT had the best predictive validity.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".