Prognostic Implications of Warfarin Cessation After Major Trauma
Bibliographic record
Abstract
BACKGROUND: Warfarin therapy is often withheld from elderly patients who fall or otherwise experience injury because of concerns regarding the long-term risk of hemorrhage in these individuals. We studied whether stopping warfarin after trauma is associated with a higher risk of subsequent adverse cardiovascular events. METHODS AND RESULTS: We conducted a retrospective, population-based, cohort study using linked administrative databases in the province of Ontario, Canada for the years 1992 to 2001. A total of 8450 elderly patients (age >65 years) who survived an incident of major trauma and were receiving warfarin before injury were followed up for a mean of 3.3 years. During the 6-month interval after trauma, 1827 (22%) patients discontinued warfarin, whereas 6623 (78%) patients continued warfarin. Warfarin cessation was not associated with an increased risk of subsequent stroke (hazard ratio [HR] 0.99, 95% CI 0.82 to 1.21) or myocardial infarction (HR 0.94, 95% CI 0.74 to 1.20) but was associated with a lower risk of major hemorrhage (HR 0.69, 95% CI 0.54 to 0.88) and a higher risk of venous thromboembolism (HR 1.59, 95% CI 1.07 to 2.36). Adjustment for baseline demographics, stroke risk factors, other comorbidities, and characteristics of the trauma did not materially change these findings. On-treatment analyses yielded similar results. CONCLUSIONS: Cessation of warfarin in elderly patients after major trauma was not associated with an increased risk of arterial thrombotic events but was associated with a significantly increased risk of venous thromboembolism.
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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 | 0.000 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".