Risk for Arterial Thromboembolic Events (ATEs) in Patients with Advanced Urinary Tract Cancer (aUTC) Treated with First-Line Chemotherapy: Single-Center, Observational Study
Bibliographic record
Abstract
Arterial thromboembolism has been associated with cancer or its treatment. Unlike venous thromboembolism, the incidence and risk factors have not been extensively studied. Here, we investigated the incidence of arterial thromboembolic events (ATEs) in an institutional series of advanced urinary tract cancer (aUTC) treated with cytotoxic chemotherapy. The ATE definition included peripheral arterial embolism/thrombosis, ischemic stroke and coronary events. A total of 354 aUTC patients were analyzed. Most patients (95.2%) received platinum-based chemotherapy. A total of 12 patients (3.4%) suffered an ATE within a median time of 3.6 months from the start of chemotherapy. The most frequent ATE was ischemic stroke (n = 7). Two ATEs were fatal. The 6-month and 24-month incidence were 2.1% (95% confidence interval [CI]: 0.9-4.1) and 3.6% (95% CI: 1.9-6.2), respectively. Perioperative chemotherapy increased the risk for ATE by 5.55-fold. Tumors other than UTC and pure non-transitional cell carcinoma histology were also independent risk factors. No association with the type of chemotherapy was found. Overall, ATEs occur in 4.6% of aUTC patients treated with chemotherapy and represent a clinically relevant manifestation. Perioperative chemotherapy significantly increases the risk for ATE. The role of prophylaxis in high-risk groups should be prospectively studied.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".