The Predictive Significance of Prognostic Nutritional Index and Serum Albumin/Globulin Ratio on the Overall Survival of Penile Cancer Patients Undergoing Penectomy
Bibliographic record
Abstract
Objective: To assess the value of using the prognostic nutritional index (PNI) and serum albumin/globulin ratio (AGR) in predicting the overall survival (OS) of patients with penile cancer (PC) undergoing penectomy. Materials and methods: A retrospective analysis of 123 patients who were admitted to our hospital due to PC from April 2010 to September 2021 and who underwent penectomy were included in the study. The optimal cut-off value of the PNI and AGR was determined by receiver operating characteristic curve analysis. Kaplan–Meier analysis and the Cox proportional hazard model were used to evaluate the correlation between the PNI, AGR, and OS in patients with PC. Results: A total of 16 of the 123 patients died during the follow-up period, and the median follow-up time was 58.0 months. The best cut-off values of the PNI and AGR were set to 49.03 (95% confidence interval 0.705–0.888, Youden index = 0.517, sensitivity = 57.9%, specificity = 93.7%, p < 0.001) and 1.28 (95% confidence interval 0.610–0.860, Youden index = 0.404, sensitivity = 84.1%, specificity = 56.2%, p = 0.003). The Kaplan–Meier analysis showed that the OS of the patients in the high PNI group and the high AGR group was significantly higher than that of the patients in the low PNI group and the low AGR group (p < 0.001). The univariable analysis showed that the aCCI, the clinical N stage, the pathological stage, and the PNI, AGR, SII, and PLR are all predictors of OS in patients with PC (p < 0.05). The multivariable analysis showed that the PNI (risk rate [HR] = 0.091; 95% CI: 0.010–0.853; p = 0.036) and the AGR (risk rate [HR] = 0.171; 95% CI: 0.043–0.680; p = 0.012) are independent prognostic factors for predicting OS in patients with PC undergoing penectomy. Conclusions: Both the PNI score and the serum AGR are independent prognostic factors for predicting OS in patients with PC undergoing penectomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".