Thromboembolism in Patients with Bladder Cancer: Incidence, Risk Factors and Prevention
Bibliographic record
Abstract
Patients with bladder cancer are at high risk of developing both venous and arterial thromboembolic events. Factors that contribute to this phenomenon include the hypercoagulable state induced by the malignancy itself, medical comorbidities that are common in this predominantly elderly patient population as well as treatments such as prolonged pelvic surgery and cisplatin-based chemotherapy. While formal guidelines address prevention of venous thromboembolism in patients undergoing radical cystectomy, consensus regarding the role of pharmacologic prophylaxis in patients with bladder cancer being treated with chemotherapy, either with neoadjuvant or adjuvant intent in conjunction with radical cystectomy, as part of bladder preservation protocols or for metastatic disease, has proved elusive. The present narrative review was undertaken to define the incidence of and identify risk factors for thromboembolism among patients with bladder cancer, as well as to assess the efficacy of pharmacologic prophylaxis in reducing the risk of thromboembolism in this patient population.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".