A propensity score analysis of radical cystectomy versus bladder-sparing trimodal therapy in the setting of a multidisciplinary bladder cancer clinic.
Bibliographic record
Abstract
e16003 Background: Multidisciplinary management improves complex treatment decision making in cancer care, but its impact for bladder cancer (BC) has not been documented. While radical cystectomy (RC) is currently viewed as the standard of care for muscle-invasive bladder cancer (MIBC), radiotherapy-based, bladder-sparing trimodal therapy (TMT) combining transurethral resection of bladder tumor, chemotherapy for radiation sensitization and external beam radiotherapy has emerged as a valid treatment option. In the absence of randomized studies, we compared the oncological outcomes between patients managed by RC or TMT using a propensity-score matched cohort analysis. Methods: Patients seen in our multidisciplinary bladder cancer clinic (MDBCC) from 2008 to 2013 were retrospectively reviewed. Those who received TMT for MIBC were identified and matched (for gender, cT and cN stage, ECOG status, Charlson comorbidity score, treatment date, age, CIS, hydronephrosis) using propensity scores, to patients who underwent RC. Overall survival and disease-specific survival (DSS) were assessed with Cox Proportional hazards modeling and competing risk analysis, respectively. Results: 112 patients with MIBC were included after matching, 56 treated with TMT and 56 by RC. Median age was 68.0 years and 29.5% were cT3/cT4. At a median follow up of 4.51 years, there were 20 (35.7%) deaths (13 from BC) in the RC group and 22 (39.3%) deaths (13 from BC) in the TMT group. 5 year DSS was 73.2% and 76.6%, in the RC and TMT groups, respectively (p = 0.49). Salvage cystectomy was performed in 6/56 TMT patients (10.7%). Conclusions: In the setting of a MDBCC, TMT yielded survival outcomes similar to matched RC patients. Appropriately selected MIBC patients should be offered the opportunity to discuss various treatment options including organ-sparing TMT.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".