Identification of characteristics predictive of long-term survival with durvalumab or durvalumab plus tremelimumab in metastatic urothelial carcinoma
Bibliographic record
Abstract
BACKGROUND: This retrospective analysis of data from clinical trials in metastatic urothelial carcinoma (mUC) was conducted to determine baseline patient characteristics associated with long-term survival (LTS) following treatment with immune checkpoint inhibitors. METHODS: Data for this analysis were from patients with platinum-refractory mUC who received durvalumab or durvalumab plus tremelimumab in phase 1/2 studies. The primary outcome measure was LTS. Patients were categorised as overall survival (OS) ≥ 2 years (from first dose) or OS < 2 years. A univariable analysis assessed independent associations with LTS and multivariable logistic regression was employed including each variable with P ≤ 0.05 as covariates. RESULTS: Among 360 patients, 88 (24.4%) had OS ≥ 2 years and 272 (75.6%) had OS < 2 years. In univariable analysis, several baseline characteristics and laboratory measurements were associated with LTS including sex, ECOG PS, PD-L1 expression, prior surgery, time from initial diagnosis, lymph node-only involvement, visceral disease, haemoglobin level, absolute neutrophil count, neutrophil-lymphocyte ratio and lactate dehydrogenase level. In multivariable analysis, LTS was significantly associated with ECOG PS, PD-L1 expression, haemoglobin level and absolute neutrophil count. CONCLUSIONS: Several baseline clinical characteristics and laboratory measurements were associated with LTS for patients with platinum-refractory mUC treated with durvalumab or durvalumab plus tremelimumab.
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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 | 0.003 |
| 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".