VALUE AND APPLICABILITY OF LARGE ADMINISTRATIVE HEALTHCARE DATABASES IN PROSTHETICS AND ORTHOTICS OUTCOMES RESEARCH
Bibliographic record
Abstract
The goal of health economics and outcomes research is to improve healthcare decision making. In the absence of high-value clinical data, the availability and quality of administrative healthcare data could be vital in the generation of evidence for orthotics and prosthetics services. The purpose of this article is to provide a stronger understanding of administrative healthcare data analysis, an area that has been scarcely examined within prosthetics and orthotics despite the wealth of information available within such data. Examples of common datasets in this arena currently available are provided, as well as an overview of the limitations and advantages of studies utilizing such datasets. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35958/28315 How To Cite: Miller TA, Wurdeman S. Value and applicability of large administrative healthcare databases in prosthetics and orthotics outcomes research. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.4. https://doi.org/10.33137/cpoj.v4i2.35958 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 | 0.351 | 0.658 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".