A comparative evaluation of the Global Leadership Initiative on Malnutrition vs the Patient‐Generated Subjective Global Assessment in assessing nutrition status in patients diagnosed with terminal cancer: A retrospective study
Bibliographic record
Abstract
This study aimed to evaluate the clinical utility of the Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition screening tool and the Global Leadership Initiative on Malnutrition (GLIM) criteria as a diagnostic framework in a cohort of patients with terminal cancer. This single-institution, retrospective cohort study included adults who were diagnosed with cancer and a predicted life expectancy <3 months intolerant to anticancer treatment who received palliative care between October 2023 and March 2024. Of 104 patients screened, 78 (54% male) were included in the analysis and 26 were excluded because of a terminal condition that precluded completion of the PG-SGA. The median age, body mass index, and survival were 73 years, 20.4, and 32 days, respectively. Weight loss occurred in 46% of patients within the previous 3 to 6 months, whereas 17% gained weight. Within the previous 2 weeks, 28% exhibited weight gain. The GLIM classified 35% of patients as well nourished, whereas the PG-SGA identified none as such. Agreement between the two tools was low (kappa coefficient = 0.037). Between the nutrition status screened by PG-SGA and assessed by the GLIM, no significant differences of all symptoms in Edmonton Symptom Assessment Systems or of survival outcomes were observed. In contrast, fluid retention and low handgrip strength emerged as significant predictors of mortality in Cox proportional hazards models. These findings suggest that, in patients with terminal cancer, PG-SGA may serve as a sensitive screening tool, whereas GLIM may have limited diagnostic applicability in end-of-life settings.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".