Bibliographic record
Abstract
INTRODUCTION: Survival benefits have been recently reported in meta-analyses of randomized clinical trials (RCTs) studying perioperative chemotherapy for muscle-invasive urothelial cancer. Controversy and lack of awareness of these data have diminished their impact on daily practice, and they deserve further scrutiny. MATERIALS AND METHODS: Recently published meta-analyses of RCTs studying perioperative chemotherapy for bladder cancer were narratively reviewed, along with two reports from the most recently reported RCT of neoadjuvant chemotherapy for bladder cancer. RESULTS: Two recently published individual patient data meta-analyses report that cisplatin-based combination neoadjuvant chemotherapy is associated with an absolute survival benefit of 5% at 5 years, and adjuvant chemotherapy with an absolute survival benefit of 9% at 3 years. However, the value of the adjuvant meta-analysis is limited by the available data. Positive surgical margins and fewer than 10 lymph nodes removed are associated with poorer prognosis. Pathological complete response is associated with better survival. CONCLUSIONS: Patients diagnosed with muscle-invasive urothelial cancer may benefit from perioperative chemotherapy and should be routinely referred to a medical oncologist. Surgical factors potentially have a greater impact on survival than the use of perioperative chemotherapy. RCTs studying all stages of localized muscle-invasive bladder cancer are currently enrolling patients in Canada and are a high priority.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".