Predictive value of prognostic nutritional index for outcomes of cervical cancer: A systematic review and meta‑analysis
Bibliographic record
Abstract
Cervical cancer is a major global health concern. Prognostic markers for cervical cancer have traditionally focused on tumor characteristics. However, there is a growing recognition of the importaxnce of the nutritional status of the patient as a possible prognostic indicator. The present meta-analysis aims to estimate the role of the prognostic nutritional index (PNI) in predicting overall survival (OS) and progression-free survival (PFS) in patients with cervical cancer. Medline, Google Scholar, Science Direct and Cochrane Central databases were systematically searched for studies reporting PNI in patients with cervical cancer. Inclusion criteria were applied to select relevant studies and data extraction was performed by two independent investigators. Risk of bias was assessed by the Newcastle-Ottawa Scale (NOS). The present meta-analysis included 10 studies with 2,352 participants. The pooled analysis showed that in patients with cervical cancer PNI did not have a significant prognostic utility in predicting OS [univariate hazard ration (HR): 1.38; 95% confidence interval (CI): 0.77-2.48) or PFS (univariate HR: 1.12; 95% CI: 0.44-2.68). These results were consistent even after adjusting for other confounders using multivariate analysis (pooled HR: 1.06 for OS; 95% CI: 0.64-1.76; pooled HR: 1.22 for PFS; 95% CI: 0.65-2.30). Subgroup analyses were also performed based on region, PNI cut-off, sample size, grade of evidence and treatment protocol and did not demonstrate any significant prognostic value of PNI. The funnel plot demonstrated symmetry, suggesting the absence of publication bias. The present meta-analysis indicated that PNI does not have a significant prognostic utility in predicting OS or PFS in women with cervical cancer. Further research is warranted to explore alternative nutritional indicators and identify reliable prognostic markers in this patient population.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| 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".