THE VALUE OF HEALTH ECONOMICS AND OUTCOMES RESEARCH IN PROSTHETICS AND ORTHOTICS
Bibliographic record
Abstract
The demand has increased for evidence regarding the effectiveness and value of prosthetic and orthotic rehabilitation interventions. Clinicians and managers are under pressure to provide treatment recommendations and demonstrate effectiveness through outcomes. It is often assumed that rehabilitation interventions, including the provision of custom-made and custom-fit orthotic and prosthetic devices, are beneficial to patients. Assessing the value of orthotic and prosthetic services has become more critical to continue to ensure equitable access to needed services. Health economics and outcomes research methods serve as tools to gauge the value of prosthetic and orthotic rehabilitation interventions. The purpose of this article is to provide an overview of the current need of health economics and outcomes research in orthotics and prosthetics, to introduce common economic methods that assist to generate real-world evidence, and to discusses the potential value of economic methods for clinicians and clinical practice. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35959/28326 How To Cite: Miller T.A, Wurdeman S, Paul R, Forthofer M. The value of health economics and outcomes research in prosthetics and orthotics. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.8. https://doi.org/10.33137/cpoj.v4i2.35959 Corresponding Author: Taavy A Miller, PhD, CPODepartment of Clinical and Scientific Affairs, Hanger Clinic, Austin, Texas, USA.E-Mail: tamiller@hanger.comORCID ID: https://orcid.org/0000-0001-7117-6124
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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 | 0.316 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".