Gynecological organ involvement at robot-assisted radical cystectomy in females: Is anterior exenteration necessary?
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate patient and disease variables associated with gynecological organ invasion in females with bladder cancer at the time of robot-assisted radical cystectomy (RARC). METHODS: We conducted a retrospective review of female patients who underwent robot-assisted anterior pelvic exenteration (RAAE) between 2005 and 2016. Patients were divided into two groups: those with gynecological organ involvement at RAAE and those without. Data were reviewed for perioperative and pathological outcomes. Kaplan-Meier method was used to depict survival outcomes. Multivariable stepwise regression analysis was performed to identify predictors of gynecological organ involvement. RESULTS: A total of 118 female patients were identified; 17 (14%) showed evidence of gynecological organ invasion at RAAE. Patients with gynecological organ invasion had more lymphovascular invasion at transurethral resection of bladder tumour (TURBT) (82% vs. 46%; p=0.006), trigonal tumours at TURBT (59% vs. 18%; p=0.001), multifocal disease (65% vs. 33%; p=0.01), pN+ (71% vs. 22%; p<0.001), positive surgical margins (24% vs. 4%; p=0.02), and they less commonly demonstrated pure urothelial carcinoma at TURBT (18% vs. 66%; p<0.001). On multivariate analysis, significant predictors of gynecological organ invasion were pN positive disease (odds ratio [OR] 6.48; 95% confidence interval [CI] 1.64-25.51; p=0.008), trigonal tumour location (OR 5.72; 95% CI 1.39-23.61; p=0.02), and presence of variant histology (OR18.52; 95% CI 3.32-103.4; p=0.001). CONCLUSIONS: Patients with trigonal tumours, variant histology, and nodal involvement are more likely to have gynecological organs invasion at RAAE. This information may help improve counselling of patients and better identify candidates for gynecological organ-sparing cystectomy.
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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 | 0.000 | 0.001 |
| 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".