Post-treatment prognostic model for patients (pts) with metastatic urothelial cancer (UC) treated with first-line chemotherapy.
Bibliographic record
Abstract
256 Background: Models to predict the outcome of pts with metastatic UC, based on pre-treatment variables, have previously been developed. However, pts often request “updated” prognostic estimates based on their response to treatment. This is particularly relevant in first-line treatment of metastatic UC, a disease state for which a fixed number of cycles of chemotherapy are typically administered. Methods: Data were pooled from 317 pts enrolled on eight trials evaluating first-line cisplatin-based chemotherapy in metastatic UC. Variables were combined in a Cox proportional hazards model to produce a nomogram to predict survival from end of treatment. The nomogram was validated externally using data from a trial of MVAC versus docetaxel plus cisplatin (n=148). Results: The median survival from end of treatment was 10.65 months [95% CI 9.20 – 13.24]; 69% of patients had died. Baseline (white blood count, ECOG performance status, number of visceral metastatic sites) and post-treatment (treatment response, duration of treatment, reason for treatment discontinuation) variables were evaluated. The Cox proportional hazard model is shown in the Table. The duration of treatment and reason for treatment discontinuation were not significantly associated with survival. The four significant variables were included in a nomogram. The nomogram achieved a bootstrap-corrected concordance index of 0.68. Upon external validation, the nomogram achieved a concordance index of 0.67. Conclusions: A model derived from pre- and post-treatment variables was constructed to predict survival from the end of first-line chemotherapy in pts with metastatic UC. This model may be useful for pt counseling and for stratification of trials exploring “maintenance” treatment. [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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".