A BRIEF INTRODUCTION TO GAME THEORY AND ITS POTENTIAL IMPLICATIONS FOR THE ECONOMICS OF ORTHOTICS & PROSTHETICS
Bibliographic record
Abstract
The economic viability of Orthotics & Prosthetics (O&P) service provision is an important concern for policy makers, patients, and practitioners. Against the background of limited funds that can be distributed for healthcare expenses overall, it is critical to identify the most cost-effective treatment options within and across disciplines, including surgical and pharmacological interventions. When those decisions are being negotiated, whether in the context of an individual case in the clinic or of general payer policies that allocate spending budgets, the O&P discipline is often perceived to be at a disadvantage due to its relatively young age, underdeveloped evidence base, and small economic clout as compared to other fields. Such asymmetrical negotiations have been the subject of economic theories and mathematical models, such as the “Game theory”, work on which has been awarded with several Nobel Prizes and other recognitions across the years. In this paper, we are introducing core concepts of this theory and discuss how they may be applied in negotiations on treatment approaches and reimbursement schedules with the goal to improve outcomes for the O&P profession. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36661/28347 How To Cite: Fiedler G, Schikorra A. A brief introduction to game theory and its potential implications for the economics of orthotics & prosthetics. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.18. https://doi.org/10.33137/cpoj.v4i2.36661 Corresponding Author: Dr. Goeran Fiedler, PhDDepartment of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA 15206, USA.E-Mail: gfiedler@pitt.eduORCID number: https://orcid.org/0000-0003-1532-1248
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".