Diagnosis of VTE postdischarge for major abdominal and pelvic oncologic surgery: implications for a change in practice
Bibliographic record
Abstract
BACKGROUND: Extended thromboprophylaxis after hospital discharge following cancer surgery has been shown to reduce the incidence of venous thromboembolism (VTE); however, this practice has not been universally adopted. We conducted a population-based analysis to determine the proportion of patients with symptomatic VTE diagnosed within 90 days after initial discharge following major abdominopelvic cancer surgery who might have benefited from extended thromboprophylaxis. METHODS: We used the Manitoba Cancer Registry to identify patients who underwent major abdominopelvic cancer surgery between 2004 and 2009. The proportion in whom VTE was diagnosed during the initial hospital stay was determined by accessing the Hospital Separations Abstracts. The proportion in whom VTE was diagnosed after discharge was determined by examining repeat admissions within 90 days and by accessing Drug Programs Information Network records for newly prescribed anticoagulants. Detailed tumour and treatment-specific data allowed calculation of VTE predictors. RESULTS: Of 6612 patients identified, 106 (1.60%) had VTE diagnosed during the initial stay and 96 (1.45%) presented with VTE after discharge. Among patients in whom VTE developed after discharge, 33.3% had a pulmonary embolus, 24% had deep vein thrombosis, and 6.3% had both. Predictors of presenting with VTE after discharge within 90 days of surgery included advanced disease, presence of other complications, increased hospital resource utilization, primary tumours of noncolorectal gastrointestinal origin and age younger than 45 years. The development of VTE was an independent predictor of decreased 5-year overall survival. CONCLUSION: The cumulative incidence of VTE within 90 days of major abdominopelvic oncologic surgery was 3.01%, with about half (1.45%) having been diagnosed within 90 days after discharge.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".