Oncological Surveillance After Radical Cystectomy: a Narrative Review of the Enhanced Recovery After Surgery Cystectomy Committee
Bibliographic record
Abstract
Purpose: Follow-up after cystectomy aims to detect relapse, but there are discrepancies in recommendations among guidelines. Routine follow-up for asymptomatic recurrences in urothelial cancer is primarily based on nonvalidated risk factors from retrospective cohort studies in single institutions. This review provides an overview of follow-up investigations, schedules, and potential risk factors of recurrence. Materials and methods: We conducted a narrative literature search on PubMed and reviewed guidelines (European Society for Medical Oncology, European Association of Urology, National Comprehensive Cancer Network, American Urology Association, and National Institute for Health and Care Excellence) and institutional protocols for cystectomy patients. Results: Our analysis included 29 studies with 23,218 patients. Most relapses occurred within 2 years, either locally or as distant recurrences in the chest, liver, bones, or brain. Factors increasing relapse risk included higher tumor stage, nodal involvement, histological subtypes, and lymphovascular invasion. Surveillance protocols varied in frequency and type of investigation. Limited recommendations were available for patients with ypT0, pT0, or non–muscle-invasive bladder cancer. Conclusions: Further research is needed to evaluate the impact of postcystectomy follow-up protocols on oncological outcomes and establish optimal surveillance procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".