GLIM‐defined malnutrition and overall survival in cancer patients: A meta‐analysis
Bibliographic record
Abstract
Abstract Background Malnutrition defined by the Global Leadership Initiative on Malnutrition (GLIM) has been associated with cancer mortality, but the effect is limited and inconsistent. We performed this meta‐analysis aiming to assess this relationship in patients with cancer. Methods We systematically searched Embase, PubMed, Web of Science, Cochrane, CINAHL, CNKI, Wanfang, and VIP databases from January 1, 2019, to July 1, 2022. Studies evaluating the prognostic effect of GLIM‐defined malnutrition on cancer survival were included. A fixed‐effect model was fitted to estimate the combined hazard ratio (HR) with a 95% CI. Heterogeneity of studies was analyzed using the I2 statistic. Quality assessment were performed using the Newcastle‐Ottawa Scale (NOS) and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. Results The search strategy identified 4378 articles in all databases combined. Nine studies (8829 patients) meeting the inclusion criteria were included for quantitative analysis. Meta‐analysis revealed significant associations between GLIM‐defined pooled malnutrition (HR = 1.75; 95% CI, 1.43–2.15), moderate malnutrition (HR = 1.44; 95% CI, 1.29–1.62), and severe malnutrition (HR = 1.79; 95% CI, 1.58–2.02) with all‐cause mortality. Sensitivity analysis supported the robustness of these associations. The between‐study heterogeneity was low (all I2 < 50%), and study quality assessed with NOS was high (all scores > 6). The evidence quality according to the GRADE tool was very low. Conclusions Our meta‐analysis suggests a significant negative association of malnutrition, as defined by the GLIM, with overall survival in patients with cancer. However, definitive conclusions cannot be made, owing to the low quality of the source data.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".