Role of Age, Comorbidity, and Frailty in the Prediction of Postoperative Complications After Surgery for Vulvar Cancer: A Retrospective Cohort Study with the Development of a Nomogram
Bibliographic record
Abstract
Surgery is the cornerstone of vulvar cancer treatment, but it is associated with a significant risk of complications that may impact prognosis, particularly in older patients with multiple comorbidities. The objective of this study was to evaluate the role of age, comorbidities, and frailty in predicting postoperative complications after vulvar cancer surgery and to develop a predictive nomogram. A retrospective cohort study was conducted, including patients who underwent surgery for vulvar cancer at two Italian institutions from January 2018 to December 2023. A logistic regression model for the rate of Clavien-Dindo 2+ 30-days complications was run, considering the age-adjusted Charlson Comorbidity Index (AACCI), body mass index (BMI), and frailty as exposures. Lesion characteristics and surgical procedures were considered as confounders. Among the 225 included patients, 50 (22.2%) had a grade 2+ complication. The predictive score of the nomogram ranged from 44 to 140. The AACCI (0-64 points) and BMI (0-100 points) were independently associated with a risk of complications. A nomogram including the AACCI and BMI predicts the risk of complications for patients undergoing surgery for vulvar cancer. The preoperative determination of the risk of complications enables surgical planning and allows a tailored peri- and postoperative management plan.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".