Complementarity of nutrition risk screening tools with malnutrition diagnosis in patients with cancer: A 12‐month follow‐up study assessing accuracy metrics and mortality
Bibliographic record
Abstract
BACKGROUND: The Global Leadership Initiative on Malnutrition (GLIM) criteria for diagnosing malnutrition were established to provide a standardized approach for diagnosing malnutrition in clinical practice using a nutrition screening tool (NST) as the first step for this process. This study aimed to compare the complementarity of NSTs with the GLIM criteria for malnutrition diagnosis in patients with cancer. METHODS: Hospitalized patients with different cancer types were evaluated in a prospective cohort study in which they were initially screened using the Patient-Generated Subjective Global Assessment (PG-SGA), Protocol for Nutritional Risk in Oncology (PRONTO), Malnutrition Universal Screening Tool, Nutritional Risk Screening 2002, Malnutrition Screening Tool, and NutriScore for nutrition risk. Malnutrition diagnosis involved phenotypic and etiological criteria as proposed by the GLIM. Complementarity of NST to GLIM criteria was evaluated by calculating accuracy metrics and investigating association with 12-month mortality. RESULTS: Nutrition risk ranged from 14.8% (NutriScore) to 82.8% (PRONTO) and frequency of malnutrition from 13.8% (with NutriScore) to 88.9% (with PG-SGA). NutriScore presented the lowest negative predictive value (25.1%) whereas PG-SGA presented the highest (58.32%). Regardless of the NST applied, the risk of malnutrition and diagnosis of malnutrition according to the GLIM criteria, combined or isolated, increased the risk of 12-month mortality. CONCLUSION: All NSTs presented low negative predictive value when their complementarity to GLIM criteria for malnutrition diagnosis was tested. Indeed, patients "at risk" presented similar increased risk of 12-month after discharge mortality in comparison with those at risk and malnourished by the GLIM criteria when all NSTs were applied.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".