Association between CONUT scores and survival outcomes in patients with non-small cell lung cancer: meta-analysis from 4973 Asian cases
Bibliographic record
Abstract
Background: The controlling nutritional status (CONUT) score is associated with cancer prognosis. However, a consensus on its prognostic value in patients with non-small cell lung cancer(NSCLC) is lacking. The present study aims to investigate the relationship between the CONUT score and prognostic and clinicopathological features of NSCLC. Methods: The PubMed, Embase, Web of Science and Cochrane Library were searched up to July 2024. Two researchers used the Newcastle Ottawa Scale (NOS) score to evaluate the quality of the included studies and extracted data. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS), and the hazard ratio (HR) and 95% confidence interval (CI) were pooled for meta-analysis. The pooled odds ratio (OR) and 95% CI were used to estimate the correlation between the CONUT score and clinical characteristics. Subgroup analysis and sensitivity analysis were performed during the pooled analysis.Funnel plots as well as Begg's and Egger's tests were used to assess publication bias. Results: Fifteen high-quality studies with 4973 patients were included. The results indicated that a high CONUT score was associated with poor OS (HR = 1.84, 95%CI: 1.55-2.18; P < 0.0001) and DFS (HR=2.40, 95%CI: 1.73-3.34; P < 0.0001).In addition, a high CONUT score was significantly related to male, advanced age, high CEA, and later TNM stage. Conclusion: The results of our meta-analysis suggest that a high CONUT score predicts a poor prognosis of NSCLC patients. In clinical practice, the CONUT score could act as an valuable tool to predict clinical outcomes in patients with NSCLC. Systematic Review Registration: https://inplasy.com, identifier INPLASY202408280100.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| 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".