MEDICAL DEVICE PRICES IN ECONOMIC EVALUATIONS
Bibliographic record
Abstract
OBJECTIVES: Economic evaluations, although not formally used in purchasing decisions for medical devices in Canada, are still being conducted and published. The aim of this study was to examine the way that prices have been included in Canadian economic evaluations of medical devices. METHODS: We conducted a review of the economic concepts and implications of methods used for economic evaluations of the eleven most implanted medical devices from the Canadian perspective. RESULTS: We found Canadian economic studies for five of the eleven medical devices and identified nineteen Canadian studies. Overall, the device costs were important components of total procedure cost, with an average ratio of 44.1 %. Observational estimates of the device costs were obtained from buyers or sellers in 13 of the 19 studies. Although most of the devices last more than 1 year, standard costing methods for capital equipment was never used. In addition, only eight studies included a sensitivity analysis for the device cost. None of the sensitivity analyses were based on actual price distributions. CONCLUSIONS: Economic evaluations are potentially important for policy making, but although they are being conducted, there is no standardized approach for incorporating medical device prices in economic analyses. Our review provides suggestions for improvements in how the prices are incorporated for economic evaluations of medical devices.
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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 | 0.386 | 0.760 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| 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".