Robot-assisted radical cystectomy with intracorporeal urinary diversion versus open radical cystectomy (iROC): protocol for a randomised controlled trial with internal feasibility study
Bibliographic record
Abstract
INTRODUCTION: Bladder cancer (BC) is a common malignancy and one of the most expensive to manage. Radical cystectomy (RC) with pelvic lymphadenectomy is a gold standard treatment for high-risk BC. Reductions in morbidity and mortality from RC may be achieved through robot-assisted RC (RARC). Prospective comparisons between open RC (ORC) and RARC have been limited by sample size, use of extracorporeal reconstruction and use of outcomes important for ORC. Conversely, while RARC is gaining in popularity, there is little evidence to suggest it is superior to ORC. We are undertaking a prospective randomised controlled trial (RCT) to compare RARC with intracorporeal reconstruction (iRARC) and ORC using multimodal outcomes to explore qualitative and quantitative recovery after surgery. METHODS AND ANALYSIS: iROC is a multicentre prospective RCT in English National Health Service (NHS) cancer centres. We will randomise 320 patients undergoing RC to either iRARC or ORC. Treatment allocation will occur after trial entry and consent. The primary outcome is days alive and out of hospital within the first 90 days from surgery. Secondary outcomes will measure functional recovery (activity trackers, chair-to-stand tests and health related quality of life (HRQOL) questionnaires), morbidity (complications and readmissions), cost-effectiveness (using EuroQol-5 Domain-5 levels (EQ-5D-5L) and unit costs) and surgeon fatigue. Patients will be analysed according to intention to treat. The primary outcome will be transformed and analysed using regression. All statistical assumptions will be investigated. Secondary outcomes will be analysed using appropriate regression methods. An internal feasibility study of the first 30 patients will evaluate recruitment rates, acceptance of randomised treatment choice, compliance outcome collection and to revise our sample size. ETHICS AND DISSEMINATION: The study has ethical approval (REC reference 16/NE/0418). Findings will be made available to patients, clinicians, funders and the NHS through peer-reviewed publications, social media and patient support groups. TRIAL REGISTRATION NUMBERS: ISRCTN13680280 and NCT03049410.
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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 | 0.056 | 0.057 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".