Low-Molecular-Weight Heparin Versus Unfractionated Heparin for Prophylaxis of Venous Thromboembolism in Medicine Patients—A Pharmacoeconomic Analysis
Bibliographic record
Abstract
BACKGROUND: Prevention of in-hospital venous thromboembolism (VTE) is identified internationally as a priority to improve patient safety. Advocated alternatives include low-dose unfractionated heparin (UFH) or low-molecular-weight heparin (LMWH). Although LMWHs are as effective as UFH, less frequent administration and potentially safer adverse effect profile associated with LMWHs might off-set greater drug acquisition costs. The objective of this study was to determine the most cost-effective thromboprophylaxis strategy for hospitalized medicine patients and specific subgroups in Canada. METHODS: A decision-analytic model assessed costs and outcomes of LMWH compared to UFH for thromboprophylaxis in at-risk hospitalized medicine patients from an institutional perspective. The outcome of interest was the incremental cost-effectiveness ratio (ICER) for preventing deep vein thrombosis (DVT) and combined untoward events (pulmonary embolism [PE], major bleed, and death). The time horizon of the model was the hospital stay. RESULTS: In the base-case analysis, LMWH thromboprophylaxis resulted in higher costs ($7.40), but 3.6 and 1.1 fewer DVT and untoward events per 1000 patients, respectively, with associated ICERs of $2042 and $6832. Results remained predominantly stable when alternative assumptions were evaluated in the sensitivity analysis. Low-molecular-weight heparin had the most favorable economic profile in patients with a history of DVT. In the probabilistic sensitivity analysis, in 33% of simulations LMWH was less costly and more effective, whereas the reverse was true for UFH only in 13% of simulations. CONCLUSIONS: Low-molecular-weight heparin administration is a cost-effective alternative for thromboprophylaxis strategy in Canadian hospitalized medicine patients.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".