Impact of histology and toxicities on outcomes of patients with muscle invasive bladder cancer receiving neoadjuvant chemotherapy.
Bibliographic record
Abstract
540 Background: Cisplatin-based neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) extends survival in muscle invasive bladder cancer (MIBC) patients (pts). Pathologic complete response (pCR) is associated with survival. We conducted a retrospective study to examine the prognostic impact of other variables including histologic subtype, location, multifocality, margins, size of tumor and toxicities. Methods: Pts who underwent RC at Dana-Farber for MIBC stage T2-T4N0-1 were studied. Data were collected for demographics, clinical and pathologic variables. Descriptive stats were reported, and Cox proportional hazards regression analyses were conducted to examine the association with recurrence-free survival (RFS) and overall survival (OS). Results: From 2002 to 2018, 150 patients were available. The median age was 66 (range 36-89) and 102 (68%) were male. MVAC/dose dense MVAC, GC and other non-standard regimens were given in 42 (28%), 85 (56.7%) and 23 (15.3%) pts, respectively. The 2-yr RFS was 63.6%, the 5-yr OS was 68.7% and pCR occurred in 38 pts (25.3%). Multivariable analysis identified pure urothelial carcinoma in the residual tumor and absence of pathologic response to be associated with poor RFS and OS. Positive margins were associated with poor RFS, while grade ≥3 toxicities were associated with poor OS. Conclusions: Pure urothelial carcinoma histology was associated with worse RFS and OS following RC after NAC for MIBC, suggesting molecular studies may be useful in these cases. The association of severe toxicities with poor OS suggests that optimal pt selection for NAC and early recognition of toxicities is important.[Table: see text]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".