Understanding Cost Effectiveness: Money Matters?
Bibliographic record
Abstract
Economic analysis is an important component that should be implemented when evaluating a new medical device. A new medical device should be both effective in improving patient outcomes as well as cost effective before it is implemented into clinical practice. This paper begins with an overview on the different methods of economic analysis including cost-minimization analysis, cost-effectiveness analysis, cost-utility analysis, and cost-benefit analysis. The second section provides a description of key design issues in cost-effectiveness analyses that are relevant to medical device trials including the perspective of the economic evaluation, the collection of cost data, how to establish clinical effectiveness in an economic analysis, how to conduct a sensitivity analysis, and when it is necessary to discount costs. It is important and necessary to consult with a health economist to ensure that the appropriate methodology is followed when conducting an economic evaluation. In conclusion, since most jurisdictions have limited funding available for health care, money definitely matters. If the cost of a medical device is unreasonable or if funding is not available, it will likely not be able to be implemented, regardless of its effectiveness. A well-conducted economic analysis will be able to answer questions on the medical device's efficacy and cost effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".