Thromboprophylaxis during neoadjuvant chemotherapy for bladder cancer reduces thromboembolism and bleeding
Bibliographic record
Abstract
OBJECTIVES: To assess the risk of venous thromboembolic events (VTEs) and bleeding with or without thromboprophylaxis during neoadjuvant chemotherapy in bladder cancer patients scheduled for radical cystectomy. MATERIALS AND METHODS: We conducted a retrospective cohort study in 4886 patients with non-metastatic bladder cancer undergoing cystectomy across 28 centres in 13 countries between 1990 and 2021. Inverse probability weighting analyses were performed to estimate the effect of thromboprophylaxis on VTE and bleeding. RESULTS: In 147 patients (3%) VTEs were recorded within the first year. These occurred a median (interquartile range [IQR]) of 127 (82-198) days after bladder cancer diagnosis. Bleeding events occurred in 131 patients (3%) within the first year. These occurred a median (IQR) of 101 (83-171) days after cancer diagnosis. In inverse probability weighting analyses, compared to patients without thromboprophylaxis during chemotherapy, patients with thromboprophylaxis had not only a lower risk of VTE (hazard ratio [HR] 0.32, 95% confidence interval [CI] 0.12-0.81; P = 0.016) but also a lower bleeding risk (HR 0.03, 95% CI 0.09-0.12; P <0.0001). The retrospective nature of the study was its main limitation. CONCLUSIONS: In this retrospective analysis, the benefit of thromboprophylaxis during neoadjuvant chemotherapy before cystectomy is in line with data from randomised trials in other malignancies. Our data suggest thromboprophylaxis is protective against VTEs and should be the standard of care during neoadjuvant chemotherapy.
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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 | 0.007 |
| 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".