A probabilistic cost-effectiveness analysis of enoxaparin versus unfractionated heparin for the prophylaxis of deep-vein thrombosis following major trauma.
Bibliographic record
Abstract
BACKGROUND: In the absence of major contraindications, treatment guidelines recommend that, following a major traumatic event, all patients receive low molecular weight heparin (e.g. enoxaparin) as thromboprophylaxis for the prevention of deep vein thrombosis (DVT). OBJECTIVE: To estimate the incremental cost-effectiveness of enoxaparin versus low dose unfractionated heparin (UH) for the prophylaxis of DVT following major trauma. METHODS: Using probabilistic decision-analytic modeling, we estimated the incremental cost-effectiveness of enoxaparin versus unfractionated heparin for the prophylaxis of DVT following moderate to severe trauma (injury severity score > or = 9) over a life-time time horizon from the perspective of the health care payer. Cost effectiveness was calculated based on both the incremental cost (ïC) per DVT averted and the ïC per life year gained (LYG). RESULTS: The incremental cost of enoxaparin relative to UH was C$90, and the incremental effectiveness was 0.085 DVTs averted and -0.13 LYG. This resulted in an incremental cost-effectiveness ratio of C$1,059 per DVT averted, and the conclusion that UH is the dominant strategy in terms of LYG. In addition to the probabilistic analysis, one-way and two-way sensitivity analysis revealed that the model was most sensitive to variation in the discount rate (3-7%), but that UH remained the dominant strategy in terms of life years independent of the parameter estimates. CONCLUSIONS: Although enoxaparin appears to be a cost-effective alternative when considering the intermediate endpoint of DVTs averted, it may be dominated by UH in terms of LYG due to the higher incidence of major bleeds in patients receiving enoxaparin versus UH.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".