C-reactive protein as a prognostic factor in advanced urothelial carcinoma receiving chemotherapy or immunotherapy.
Bibliographic record
Abstract
436 Background: Clinical prognostic factors have been reported for patients receiving systemic chemotherapy. We hypothesized that markers of tissue damage and inflammation may be prognostic. We conducted a retrospective study to evaluate the prognostic impact of serum lactate dehydrogenase (LDH) and C-reactive protein (CRP) in patients with metastatic UC. Methods: We collected data for patients with metastatic UC receiving systemic therapy and measured serum LDH and CRP at baseline before initiating therapy. The variables collected were recognized clinical prognostic factors (performance status [PS], visceral metastasis, hemoglobin, albumin) and outcomes (time to failure [TTF], overall survival [OS]). TTF was defined as time from initiation of therapy to discontinuation of agent for any reason. LDH and CRP were evaluated on a log-continuous scale. The Kaplan-Meier method was used to estimate times to events. Cox proportional hazards regression and logistic regression were used to study the association of factors with TTF and OS. All tests were 2-sided and significance was defined as p≤0.05. Results: A total of 36 patients were available with a median age of 74 years. The systemic therapy was platinum-based chemotherapy in 18 patients (50%), PD1/PD-L1 inhibitor in 8 patients (22.2%) and the remaining received other agents (taxane or investigational). Twenty-nine (80.5%) had PS 0-1, 13 (36.1%) had visceral metastasis and 16 (44.4%) had received prior platinum-based chemotherapy. A total of 31 patients were evaluable for this analysis with 5 inevaluable due to missing data or inadequate follow-up. On multivariable analyses, only CRP was associated with TTF (HR 1.55 [95% CI: 1.10, 2.20], p = 0.013) and OS (HR 1.77 [95% CI1.10, 2.85], p = 0.018). The limitations of a small dataset and retrospective analysis apply. Conclusions: In patient with advanced UC receiving systemic chemotherapy or PD1/PD-L1 inhibitors as first-line or salvage therapy, CRP was significantly prognostic for both TTF and OS, while other factors were not. Given the potentially powerful prognostic impact of CRP, investigation in larger datasets is warranted to enable better prognostic stratification.
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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 | 0.001 | 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 | 0.001 |
| 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".