Optimal pathological response after neoadjuvant chemotherapy for muscle‐invasive bladder cancer: results from a global, multicentre collaboration
Bibliographic record
Abstract
Objectives To evaluate outcomes of patients achieving a post‐treatment pathological stage of Patients and Methods Patients from 10 international centres who underwent NAC for cT2–4aN0–1 MIBC and achieved Results A total of 625 patients were included. The median age was 66 years and 80% were male. Gemcitabine and cisplatin (GC, 56%) and methotrexate, vinblastine, doxorubicin and cisplatin (MVAC)/dose‐dense (dd)MVAC (32%) were the most common NAC regimens. ypT0, pure ypTis, ypTa ±ypTis and ypT1 ± ypTis were attained in 58.1%, 20.0%, 7.6% and 14.2% of patients, respectively. The cumulative incidence of recurrence at 5 years was 9%, 16%, 29% and 30%, respectively. Pathological stage was prognostic for recurrence, with ypTa ± Tis (hazard ratio [HR] 3.20, 95% confidence interval [CI] 1.40–7.30) and ypT1 ± Tis disease (HR 4.03, 95% CI 2.13–7.63) associated with a significantly higher recurrence risk. Pure ypTis (HR 1.66, 95% CI 0.82–3.38) and the type of NAC regimen (ddMVAC: HR 1.59, 95% CI 0.55–4.56; MVAC: HR 1.18, 9%% CI 0.25–5.54; reference: GC) were not associated with recurrence. Conclusion We propose that optimal pathological response after NAC be defined as attainment of ypT0N0/ypTisN0 at RC. Patients with ypTaN0 or ypT1N0 disease (with or without Tis) at RC displayed a significantly higher risk of recurrence and may be candidates for trials investigating adjuvant therapy.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".