The economics of adjunctive therapies in coronary angioplasty: drugs, devices, or both?
Bibliographic record
Abstract
BACKGROUND: Abciximab reduces the number of ischemic events in patients undergoing angioplasty compared to standard therapy. Coronary stenting reduces the need for repeat procedures. Abciximab or stents individually are considered cost effective interventions. There is a need to quantify the economic value of the combination of abciximab and stenting over stenting alone. METHODS: A decision analytic model was developed incorporating the outcomes from the EPISTENT study. Costs from Canadian sources for hospitalization, procedures and medications were used. Life expectancy was estimated using a Markov model. Total expected costs and outcomes of the abciximab and stent vs. stent alone were compared in an incremental analysis. The perspective of the analysis was a Canadian teaching hospital. RESULTS: The acquisition cost for abciximab was partially offset by reduced costs for managing clinical events resulting in a net incremental cost of 1,076 dollars per patient over one year (8,617 dollars combination vs. 7,541 dollars stent alone). This added cost was accompanied by a reduction in large MI or death by an absolute rate of 5.7% at one year (5.3% combination vs. 11.0% stent alone), yielding an incremental cost-effectiveness ratio of 18,877 dollars per death or large MI averted. The long-term survival gain was 0.15 to 0.37 years yielding an attractive incremental cost effectiveness ratio of 2,832 dollars to 7,173 dollars per life year gained. CONCLUSIONS: The combination of abciximab and stenting versus stenting alone provides improved clinical outcomes at a very reasonable cost from the Canadian hospital perspective.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".