The Association of Preoperative Prognostic Nutritional Index With Survival Outcome in Ovarian Clear Cell Cancer
Bibliographic record
Abstract
Background: The preoperative prognostic nutritional index (PPNI) has been investigated as a prognostic indicator in various cancers including epithelial ovarian cancer (EOC). However, its prognostic relevance in epithelial ovarian clear cell cancer (EOC-CC) remains uncertain. The objective of the study was to clarify the prognostic values of PPNI in EOC-CC patients. Methods: ). The association between PPNI and survival outcome was analyzed using the Kaplan-Meier method and the Cox proportional hazard model. Results: The optimal cut-off value of PPNI, set at a mean of PPNI as 50, divided the EOC-CC patients into two groups: the low (n = 115) and the high (n = 175) PPNI group. With a median follow-up time of 63 months, patients with high PPNI exhibited significantly superior 5-year overall survival (OS) rates (76.4% vs. 56.8%, P = 0.004) and 5-year progression-free survival (PFS) rates (71.0% vs. 58.7%, P = 0.017) compared to patients with low PPNI. Univariate analysis revealed high PPNI correlated with increased OS (hazard ratio (HR): 0.51; 95% confidence interval (CI): 0.35 - 0.75) and PFS (HR: 0.63; 95% CI: 0.43 - 0.92). Nevertheless, in a multivariate analysis, high PPNI did not retain its status as an independent prognostic factor for a favorable prognosis in EOC-CC patients. Conclusion: The present study did not confirm the prognostic significance of PPNI on survival outcomes in EOC-CC patients. Therefore, conducting prospective clinical research with large samples is necessary to illustrate the predictive values of PPNI in this rare disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".