Clinical Utility of the Geriatric Nutritional Risk Index Before Surgical Intervention for Epithelial Ovarian Cancer Patients: A Retrospective Study
Bibliographic record
Abstract
Background: The aim of the study is to analyze the impact of the geriatric nutritional risk index (a patient nutritional assessment item) on the prognoses of epithelial ovarian cancer patients. Methods: In this retrospective study conducted at a single hospital, we retrospectively analyzed 75 epithelial ovarian cancer patients who underwent surgical treatment at our hospital from 2010 to 2015. The geriatric nutritional risk index cut-off value was calculated using the receiver operating characteristic curve. Patients were divided into two groups on the basis of the calculated value. Kaplan-Meier curves were prepared for each group, and the difference in survival rates was calculated using the log-rank test. Cox proportional hazards regression analysis was used to compare other factors that affect prognosis. Results: The geriatric nutritional risk index was calculated to be 97.3. The survival rate was 61.9% for the group of patients with an index value > 97.3, and 39.4% for patients with an index value < 97.3 at 48 months (P < 0.001). A univariate analysis was performed with the following variables: age > 60 years, albumin level < 3.5 g/dL, body mass index < 22, presence of ascites, cancer antigen 125 level > 35 U/mL, type of tumor tissue, residual lesion, and geriatric nutritional risk index < 97.3. Albumin level, residual lesion, and geriatric nutritional risk index showed significant differences. A multivariate analysis was also performed, and only the geriatric nutritional risk index showed a significant difference (P = 0.0481). Conclusions: The geriatric nutritional risk index may have a strong influence on the prognoses of epithelial ovarian cancer patients. We recommend utilizing these findings in daily clinical practice and incorporating them into treatment strategies for epithelial ovarian cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".