Are drug-coated balloons cost effective for femoropopliteal occlusive disease? A comparison of bare metal stents and uncoated balloons
Bibliographic record
Abstract
AIM: To perform a cost-effectiveness analysis to help hospital decision-makers with regard to the use of drug-coated balloons compared with bare metal stents and uncoated balloons for femoropopliteal occlusive disease. METHODS: Clinical outcomes were extracted from the results of meta-analyses already published, and cost units are those used in the Quebec healthcare network. The literature review was limited to the last four years to obtain the most recent data. The cost-effectiveness analysis was based on a 2-year perspective, and risk factors of reintervention were considered. RESULTS: The cost-effectiveness analysis indicated that drug-coated balloons were generally more efficient than bare metal stents, particularly for patients with higher risk of reintervention (up to CAD$1686 per patient TASC II C or D). Compared with uncoated balloons, results indicated that drug-coated balloons were more efficient if the reintervention rate associated with uncoated balloons is very high and for patients with higher risk of reintervention (up to CAD$3301 per patient). CONCLUSION: The higher a patient's risk of reintervention, the higher the savings associated with the use of a drug-coated balloon will be. For patients at lower risk, the uncoated balloon strategy is still recommended as a first choice for endovascular intervention.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".