Prognostic Value of Pre-Treatment Prognostic Nutritional Index in Esophageal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Prognostic nutritional index, combining albumin and lymphocyte counts, which represents the nutritional and immune status, was considered as a effective predictor for the patient's prognosis after surgery. To comprehensively analyze the relative effectiveness of prognostic performance of pretreatment prognostic nutritional index in esophageal cancer, we performed this meta-analysis. Methods We performed a systemic search in PubMed, EMBASE, CNKI and Web of science. The hazard ratios (HRs) or odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were extracted to explore the correlation between PNI and the postoperative survival of patients with esophageal cancer, including overall survival(OS), recurrence-free survival(RFS) and postoperative complications. The Newcastle-Ottawa Scale(NOS) was applied to estimate the quality of the included studies. The Begg's test was applied to assess the publication bias. Result A total of 13 articles with 3543 patients, were included in our meta-analysis, and 9 studies reported OS in 2731 esophageal cancer patients. The pooled results of the 9 studies suggested that esophageal cancer patients with a low prognostic nutritional index would have a worse overall survival(HR=1.14 95% CI 0.99-1.31 P<0.05). The integrated results also indicated that the prognostic nutritional index was a negative predictor for RFS. Conclusion This meta-analysis indicated a high correlation between PNI and postoperative survival of esophageal cancer. Esophageal cancer patients with low PNI value tend to have worse overall survival(OS) and may be at a higher risk of esophageal cancer recurrence. However, more relevant researches are needed to confirm the association between PNI and postoperative complications of esophageal cancer. Keywords: Prognostic nutritional index, Esophageal cancer, Prognosis, Meta-analysis, Overall survival
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| 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".