Industry Payments to Foot and Ankle-Trained Orthopedic Surgeons: An Analysis of the Open Payments Database From 2014 to 2019
Bibliographic record
Abstract
Introduction The authors examined if the transparency in industry payments to foot and ankle-trained orthopedic surgeons resulted in the following changes to the (1) median general payments to surgeons, (2) trend in median payments to surgeons across all subcategory payments, and (3) trend in median payments to surgeons in 11 regions of the United States. Methods A retrospective review of the Centers for Medicare and Medicaid Services (CMS) and Open Payments Database (OPD) was performed to identify all industry payments made by drug and medical device companies to orthopedic surgeons (N = 3,835) between January 1, 2014, and December 31, 2019. Descriptive statistics were calculated, and trend analyses in annual payments, number of payments to surgeons per year, payment subtypes, and regional distributions were analyzed. Results A total of 53,280 payments totaling $53,454,850.56 were made to orthopedic foot and ankle surgeons between 2014 and 2019. Mean and median payments were $1,003.28 and $60.19, respectively. Statistically significant differences in mean payment amounts were observed by year (p = 0.001) with a highly statistically significant, strong increase in the number of payments made over the six-year period (r = 0.97, p < 0.001). The greatest increases in median individual payments were observed for gifts (277.1%; r = 0.18, p = 0.05), education (250.6%; r = 0.17, p < 0.001), and royalties and licensing (72.1%; r = 0.05, p = 0.04). Statistically significant increasing trends in median payments over time were observed for the Northeast (p < 0.001) and South regions (p < 0.001). Discussion The results of this study demonstrate the increase in payments made across the six-year time period. The study demonstrates that there is a shift in the type of payments from speaker fees, entertainment, and lodging to education, gifts, honoraria, royalties, and consulting. Conclusion Since the OPD release, no significant decrease was identified in the financial relationship between foot and ankle surgeons and the industry; rather, an increase was observed. This increase in education, royalties, and consulting shows that more foot and ankle surgeons are getting involved in the industry, contrary to expectations. The partnership between industry and physicians can help to improve innovation and bring new ideas to the future of orthopedics.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".