Prognostic Significance of the Preoperative Prognostic Nutritional Index in Epithelial Ovarian Cancer Patients: A Systematic Review and Meta-analysis of Cohort Studies
Bibliographic record
Abstract
Abstract BackgroundThe main aim of this study was to validate the potential association between the preoperative prognostic nutritional index (PNI) and survival of patients with ovarian cancer (OC).MethodsWe systematically searched multiple databases (PubMed, EMBASE, and Web of Science) for publications up to June 30, 2019, to identify observational studies evaluating the PNI in relation to survival. Two reviewers independently extracted data and assessed the quality of each study using the Newcastle-Ottawa Scale (NOS). Summary hazard ratios (HR) and 95% confidence intervals (CI) were calculated with the aid of a random-effects model. The potential for publication bias was explored using Funnel plots as well as Begg’s and Egger’s tests.ResultsAmong the 15,000 studies selected for selection, 5 retrospective cohort studies (4 from China and one from Japan) comprising 1964 OC patients met the inclusion criteria. All studies were graded as ‘low risk of bias’ according to NOS. A low preoperative PNI was associated with poor overall survival (HR = 1.69, 95% CI = 1.16–2.46; I2 = 83.8%) and progression-free survival (HR = 1.86, 95% CI = 1.39–2.51; I2 = 29.7%) of OC patients. No significant publication bias was detected.ConclusionsCollective data from the present systematic review and meta-analysis suggest that a low preoperative PNI is associated with poor survival in OC. Further prospective studies are required to confirm these findings.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.002 | 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".