Prophylactic anticoagulation to prevent venous thromboembolism in traumatic intracranial hemorrhage: a decision analysis
Bibliographic record
Abstract
INTRODUCTION: Patients with intracranial hemorrhage due to traumatic brain injury are at high risk of developing venous thromboembolism including deep vein thrombosis (DVT) and pulmonary embolism (PE). Thus, there is a trade-off between the risks of progression of intracranial hemorrhage (ICH) versus reduction of DVT/PE with the use of prophylactic anticoagulation. Using decision analysis modeling techniques, we developed a model for examining this trade-off for trauma patients with documented ICH. METHODS: The decision node involved the choice to administer or to withhold low molecular weight heparin (LMWH) anticoagulation prophylaxis at 24 hours. Advantages of withholding therapy were decreased risk of ICH progression (death, disabling neurologic deficit, non-disabling neurologic deficit), and decreased risk of systemic bleeding complications (death, massive bleed). The associated disadvantage was greater risk of developing DVT/PE or death. Probabilities for each outcome were derived from natural history studies and randomized controlled trials when available. Utilities were obtained from accepted databases and previous studies. RESULTS: The expected value associated with withholding anticoagulation prophylaxis was similar (0.90) to that associated with the LMWH strategy (0.89). Only two threshold values were encountered in one-way sensitivity analyses. If the effectiveness of LMWH at preventing DVT exceeded 80% (range from literature 33% to 82%) our model favoured this therapy. Similarly, our model favoured use of LMWH if this therapy increased the risk of ICH progression by no more than 5% above the baseline risk. CONCLUSIONS: Our model showed no clear advantage to providing or withholding anticoagulant prophylaxis for DVT/PE prevention at 24 hours after traumatic brain injury associated with ICH. Therefore randomized controlled trials are justifiable and needed to guide clinicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".