Risk factors and timing of venous thromboembolism after radical cystectomy in routine clinical practice: a population‐based study
Bibliographic record
Abstract
OBJECTIVE: To describe the risk factors and timing of perioperative venous thromboembolism (VTE) and its association with survival for patients undergoing radical cystectomy (RC) in routine clinical practice. PATIENTS AND METHODS: The population-based Ontario Cancer Registry was linked to electronic records of treatment to identify all patients who underwent RC between 1994 and 2008; VTE events were identified from hospital diagnostic codes. Multivariate logistic regression analysis was used to determine the factors associated with perioperative VTE. A Cox proportional hazards regression model explored the associations between VTE and survival. RESULTS: Of the 3 879 patients included in the study, 3.6% (141 patients) were diagnosed with VTE at ≤1 month of their surgical admission date. This increased to 4.7% (181) at ≤2 months and 5.4% (211) at ≤3 months. In all, 55% of VTE events presented after hospital discharge. In multivariate analysis, factors associated with VTE included higher surgeon volume (P = 0.004) and increased length of hospital stay (LOS; P < 0.001). Lymph node yield and adjuvant chemotherapy were not associated with VTE. VTE was associated with an inferior cancer-specific survival [hazard ratio (HR) 1.35, 95% confidence interval (CI) 1.13-1.62] and overall survival (HR 1.27, 95% CI 1.08-1.49). CONCLUSIONS: Over half of VTE events in RC patients occur after hospital discharge, with a substantial incidence up to 3 months after surgery. Limited actionable risk factors for VTE were identified other than LOS. In this population-based cohort, VTE was associated with inferior long-term survival.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".