Exploring the approach to parameter uncertainty in early economic evaluations of surgical technology – a systematic review
Bibliographic record
Abstract
INTRODUCTION: The role of early economic evaluation (EEE) in the development of medical technology has been increasingly recognized; however, data on the use of EEE in surgical technology are sparse. The objective of this review was to explore the use of EEE in the development of surgical technologies, with emphasis on how uncertainty has been addressed. AREAS COVERED: A systematic review was conducted, and original articles employing any form of EEE of surgical technology were selected for review, with 10 studies included in the analysis. These studies demonstrated significant variation in the approach to managing parameter uncertainty, specifically regarding the type of analysis used and the inclusion of effectiveness parameters in sensitivity analysis. The conclusions drawn did not appear to factor in uncertainty in the models. EXPERT OPINION: Approaches to handling parameter uncertainty in previous EEEs of surgical technology have been limited, with some studies failing to address parameter uncertainty. In addition, EEEs do not appear to follow established guidelines with respect to the use of sensitivity analyses. It is important that EEEs of surgical technology address parameter uncertainty in order to draw more robust conclusions from the analysis and allow investors to consider this uncertainty when making investment decisions.
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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 | 0.176 | 0.489 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".