Geriatric nutritional risk index as a predictor of prognosis in hematologic malignancies: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective Recent studies have reported inconsistent results regarding the association between geriatric nutritional risk index (GNRI) and clinical outcomes in patients with hematologic malignancies (HMs). We performed a meta-analysis to evaluate the effect of low GNRI on the overall survival (OS) and progression-free survival (PFS) in patients with HMs. Research Methods and Procedures We conducted the research via PubMed, Embase, and Cochrane Library databases to identify trials. Exploring the association between GNRI and prognosis in patients with HMs. A meta-analysis of OS and PFS was performed. Quality In Prognostic Studies instrument and Newcastle–Ottawa quality assessment Scale were used to assess the quality of included trials. Results Fourteen studies enrolling 3,524 patients with HMs were included. Low GNRI was associated with shorter OS (Hazard ratio (HR) = 1.77; 95% CI = 1.44–2.18, p < 0.01) and PFS (HR = 1.63; 95% CI = 1.17–2.27, p < 0.01) in patients with HMs. In the subgroup analysis, GNRI was not significantly associated with prognosis in Chinese patients with HMs (OS, HR =1.33; 95% CI = 0.89–1.98, p = 0.16; PFS, HR = 1.70; 95% CI = 0.72–4.01, p = 0.23). For the subgroup with a GNRI cutoff value less than 98, there was no significant difference in PFS (HR = 1.34; 95% CI = 0.98–1.83, p = 0.06). Conclusion Low GNRI negatively impacted on the prognosis in patients with HMs. Prospective studies to identify the best cut-off value for GNRI are required.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".