Improved prognostic classification of patients receiving salvage systemic therapy for advanced urothelial carcinoma.
Bibliographic record
Abstract
311 Background: Previously identified prognostic factors in patients (pts) receiving salvage systemic therapy for advanced urothelial carcinoma (UC) include performance status (PS), liver metastasis (LM), hemoglobin (Hb) and time from prior chemotherapy (TFPC). Given the prognostic impact of peripheral blood neutrophils (N), lymphocytes (L), thrombocytes (T) and albumin (Alb) in other malignancies, we investigated their impact in the salvage setting of advanced UC. Methods: Phase II trials of salvage systemic therapy were utilized. Data on N, L, T and Alb were required in addition to TFPC, Hb, PS and LM status. N, L, T and Alb were categorized as normal, upper limit of normal (ULN). Cox proportional hazards regression was used to evaluate their association with overall survival (OS). An optimal regression model was constructed using forward stepwise selection and risk groups defined using number of identified adverse risk factors. Trial was a stratification factor. Results: Data was obtained from 10 trials accruing 708 pts. Of these, 682 pts had available TFPC, Hb, PS and LM status, while 631, 554, 649 and 491 had N, L, T and Alb available. Median OS was 6.8 (95% CI: 6.0-7.0) months. Neutrophilia (N>ULN), thrombocytosis (T>ULN) and hypoalbuminemia (Alb 0 and LM status, only thrombocytosis and hypoalbuminemia remained significant (Table). Risk groups were constructed. Median OS was 8.8, 6.3, 5.0 and 3.8 months for n=290, 220, 123 and 49 patients with 0-1, 2, 3 and ≥4 factors. This 6-factor prognostic model was internally validated with an improvement in the c-index from 0.564 to 0.590. Conclusions: The addition of hypoalbuminemia and thrombocytosis to TFPC, Hb, PS and LM status enhanced the prognostic risk groupings in pts receiving salvage systemic therapy for advanced UC. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".