Neoadjuvant cisplatin/gemcitabine (CG) chemotherapy for muscle-invasive transitional cell carcinoma of the bladder (TCCB): A single-institution experience.
Bibliographic record
Abstract
304 Background: Despite evidence of a survival advantage for neoadjuvant cisplatin-based chemotherapy prior to radical cystectomy (RC) for muscle-invasive TCCB, many patients are not treated. While many Canadian centers use CG chemotherapy in this setting, there is little data on pathologic response rate, often considered an important predictor of long term outcome. This retrospective study was undertaken to determine the use of neoadjuvant treatment prior to RC at our institution and assess pathologic response rates at time of RC for patients receiving neoadjuvant CG. Methods: A retrospective chart review was performed on all patients undergoing RC between January 1, 2007 and June 30, 2011 in Edmonton, Alberta, Canada. Data were collected on patient demographics, pre-treatment clinical stage information, type and amount of chemotherapy administered, and pathologic data from RC specimens. Results: A total of 251 RC were performed during the study period. Eighty-three RC were performed for non-muscle invasive TCC: 15 non-malignant causes, 27 refractory superficial TCC, 41 non-TCC malignant histology. A total of 168 RC were performed for T2-T4 TCC. Median age at diagnosis was 68 years. Ninety-two (55%) patients received neoadjuvant CG. Seventy-five (45%) patients went straight to RC. Reasons for neoadjuvant GC not being given include: medical contraindication in 43 patients (56%), patient refusal in 9 patients (12%) and lack of referral to Medical Oncologist in 24 patients (32%). Of a possible 116 patients who would have been eligible to receive neoadjuvant chemotherapy, 92/116 (79%) were treated. Of the 92 patients receiving chemotherapy prior to RC, 19 (21%) of the surgical specimens were pathologically free of cancer (pT0) at the time of surgery and 18 (20%) had only superficial disease remaining. By contrast, only three (4%) of the 75 patients who went to immediate RC achieved pT0 status and 5 (7%) had remaining superficial disease. Conclusions: The use of neoadjuvant chemotherapy prior to RC at our institution is higher than quoted in published literature. The use of neoadjuvant GC improves the chances of eliminating residual cancer in the RC specimen.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".