The controlling nutritional status score as a predictor of survival in hematological malignancies: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective The controlling nutritional status score (CONUT) has been widely used for ascertaining the prognosis of various cancers. However, its use in patients with hematological malignancies remains unclear. This review examined evidence on the utility of CONUT as a prognostic marker for patients with hematological malignancies. Methods All cohort studies that examined the association between CONUT and outcomes of hematological malignancies and were published on the databases of Embase, Scopus, CENTRAL, Web of Science, and PubMed were searched from the inception of the databases to 30 January 2024. The primary outcome was overall survival (OS), and the secondary outcome was progression-free survival (PFS). Results A total of 23 studies were available for review. A meta-analysis of 22 studies showed that high CONUT was significantly associated with poor OS in patients with hematological malignancies (HR: 1.95 95% CI: 1.62, 2.35 I 2 = 89%). The results remained unchanged on sensitivity and subgroup analyses based on study location, sample size, diagnosis, CONUT cutoff, and the Newcastle–Ottawa Scale score. Only six studies reported data on PFS, and the pooled analysis found that high CONUT was a significant marker for poor PFS in patients with hematological malignancies [hazards ratio (HR): 1.64 95% CI: 1.21, 2.20 I 2 = 70%]. These results, too, maintained significance in the sensitivity analysis. Conclusion CONUT is an independent predictor of poor OS in patients with hematological malignancies. The results appear to be valid across different cancer types and with different CONUT cutoffs. Scarce data also suggest that CONUT could predict PFS.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".