Time from prior chemotherapy (TFPC) as a prognostic factor in advanced urothelial carcinoma (UC) receiving second-line systemic therapy.
Bibliographic record
Abstract
4522 Background: Prognostic factors for overall survival (OS) in patients receiving second-line chemotherapy for advanced platinum-pretreated UC include ECOG performance status (PS) >0, hemoglobin (Hb) <10g/dL and the presence of liver metastasis (LM) (Bellmunt J, J Clin Oncol 2010). We hypothesized that time from prior chemotherapy (TFPC) independently impacts OS. Methods: Of 11 available phase II trials evaluating second-line therapy for advanced UC (n=698), 6 trials with available baseline Hb, PS and LM were utilized (n=534). The trials evaluated vinflunine (2 trials), docetaxel plus vandetanib or placebo, paclitaxel-gemcitabine, nanoparticle-albumin-bound paclitaxel and paclitaxel-cetuximab. The Kaplan-Meier method was used to estimate OS from date of starting second-line therapy. Cox proportional hazards regression stratified for trial was used to evaluate the prognostic effect of factors on OS. TFPC was evaluated as a continuous variable, and based on cutpoints of 3, 6, 9 and 12 months (mo) from prior chemotherapy to first study treatment. The choice of optimal cutpoint for TFPC was determined by the maximum likelihood ratio χ2 statistic. Results: Overall, 513 patients were evaluable. 64.1% received prior chemotherapy for metastatic disease. Median OS was 6.8 mo (95% CI: 6.1 to 7.4); range was 0 to 84.2 mo. Median OS was 5.2, 7.1, 8.8, 7.6 and 10.6 mo respectively for TFPC <3 (n=181), 3 to <6 (n=133), 6 to <9 (n=77), 9 to <12 (n=45) and >12 (n=77) mo, respectively. Shorter TFPC was independently prognostic for decreased survival. The optimal cutpoint for TFPC was <3 mo, but no well-defined plateau was observed. PS>0 (HR=1.72, p<0.001), LM (HR=1.41, p=0.002), Hb <10 g/dl (HR=1.59, p=0.001) and TFPC <3 mo (HR=1.67, p<0.001) were significantly prognostic in the multivariate model. Timing of prior chemotherapy (metastatic disease vs. perioperative) was not prognostic. Conclusions: A shorter duration of TFPC exhibited a significant negative prognostic impact on OS independent of known prognostic factors in patients receiving second-line therapy for advanced UC. If externally validated, TFPC should be a stratification factor in trials of second-line therapy for advanced UC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".