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Record W4392167551 · doi:10.7759/cureus.54981

Industry Payments to Orthopedic Surgeons Among All Subspecialties: An Analysis of the Open Payments Database From 2014 to 2019

2024· article· en· W4392167551 on OpenAlexaff
John M. Tarazi, Nicholas Frane, Alain E. Sherman, Peter B. White, Matthew J. Partan, Emma K Humphrey, Adam Bitterman

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsHeritage College
Fundersnot available
KeywordsOrthopedic surgeryPaymentSubspecialtyMedicineMedicaidFamily medicineActuarial scienceBusinessFinanceSurgeryHealth careEconomics

Abstract

fetched live from OpenAlex

Introduction Since the passage of the Physician Payments Sunshine Act in 2010, the Centers for Medicare and Medicaid Services (CMS) started the National Physician Payment Transparency Program and Open Payments Database (OPD), which allowed for public access to financial disclosures between physicians and industry. Although orthopedic surgeons receive the highest average payments when compared to other specialties, there has been limited data evaluating these payments among the different orthopedic subspecialties. The purpose of this study was to analyze all industry payments made across all subspecialties among orthopedic surgeons. Methods A retrospective review of the CMS OPD was performed to identify all industry payments made by drug and medical device companies to orthopedic surgeons (N = 28,475) between January 1, 2014, and December 31, 2019. Descriptive statistics were calculated for the number, individual value, and total value of industry payments, stratified by payment type and orthopedic subspecialty. Results A total of 1,048,573 payments (approximately $1.6 billion) were made to orthopedic surgeons between 2014 and 2019. The average orthopedic surgeon received 6.14 payments per year (SD = 29.39), with a mean individual payment amount of $1,542.32. Royalties or licensing comprised the greatest proportion of open payments, followed by consulting fees. Adult reconstruction (M = $225,131.10) and spine (M = $197,404.74) received significantly greater total payments when compared to all other subspecialties (all p-values ≤ 0.001). Differences in total payments made to trauma (M = $73,789.65), sports medicine (M = $60,988.09), foot and ankle (M = $45,007.45), pediatric orthopaedics (M = $35,898.54), general orthopaedics (M = $28,405.81), and hand (M = $14,027.76) were all found to be statistically equivalent (all p--values > 0.20). Discussion Increased collaboration between physicians and industry has resulted in the rapid advancement of innovation that can have sizeable financial implications among orthopedic surgeons. There exists significant heterogeneity in open payments made to orthopedic surgeons when stratified by subspecialty. Adult reconstructive and spine surgeons were the most compensated whereas hand and general orthopaedic surgeons received the least.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.015
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.380
GPT teacher head0.561
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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