A PRELIMINARY COST-UTILITY ANALYSIS OF THE PROSTHETIC CARE INNOVATIONS: BASIC FRAMEWORK
Bibliographic record
Abstract
A preliminary cost-utility analysis (CUA) of prosthetic care innovations can provide timely information during the early stage of product development and clinical usage. Concepts of preliminary CUAs are emerging. However, several obstacles must be overcome before these analyses are performed routinely. Disparities of methods and high uncertainty make the outcomes of usual preliminary CUAs challenging to interpret, appraise and share. These shortcomings create opportunities for a basic framework of preliminary CUAs. First, I introduced a basic framework of a preliminary CUA built around a series of constructs and hands-on recommendations. Then, I appraised this framework considering the strengths and weaknesses, barriers and facilitators, and return on investment. The design of the basic framework was determined through the review of health economic and prosthetic-specific literature. A preliminary CUA comparing the costs and utilities between usual intervention and an innovation could be achieved through a 15-step iterative process focusing on feasibility, constructs, analysis, and interpretation of outcomes. This CUA provides sufficient evidence to identify knowledge gaps and improvement areas, educate about the design of subsequent full CUAs, and obtain fast-track approval from governing bodies. Like previous CUAs, the main limitations were inherent to the constructs (e.g., narrow perspective, plausible scenarios, mid-term time horizon, substantial assumptions, data mismatch, high uncertainty). Key facilitators potentially transferable across preliminary CUAs of prosthetic care innovations included choosing abided constructs, capitalizing on prior schedules of expenses, and benchmarking baseline or incremental utilities. This new approach with preliminary CUA can simplify the selection of methods, standardize outcomes, ease comparisons between innovations, and streamline pathways for adoption. Further collegial efforts toward validating standard preliminary CUAs will facilitate access to economic prosthetic care innovations, improving the lives of individuals suffering from limb loss worldwide. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36365/28328 How To Cite: Frossard L. A preliminary cost-utility analysis of the prosthetic care innovations: basic framework. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.10. https://doi.org/10.33137/cpoj.v4i2.36365 Corresponding Author: Laurent Frossard (PhD), Professor of BionicsYourResearchProject Pty Ltd, Brisbane, Australia.E-Mail: laurentfrossard@outlook.comORCID number: https://orcid.org/0000-0002-0248-9589
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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 | 0.078 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".