Analysis of the Digital Footprint of Orthopaedic Surgeons
Bibliographic record
Abstract
INTRODUCTION: Patients increasingly rely on Google search to guide their choice of healthcare providers. Despite this trend, there is limited literature systematically characterizing the online presence of orthopaedic surgeons. The goal of this study was to identify the information patients see after queries of Google search when selecting orthopaedic surgeon providers. METHODS: The Physician Comparable downloadable file from the Centers for Medicare and Medicaid Services was deduplicated and filtered. A list of orthopaedic surgeons within the United States was generated, of which a randomized sample was taken and queried using a Google Custom Search. The results for each surgeon's first page were classified into the following categories: (1) hospital-controlled content website, (2) third-party health website, (3) social media website, (4) primary academic journals, or (5) other. RESULTS: The most frequently returned website was third-party health websites (43.3%). Statistically significant differences were observed in the categories across multiple comparisons, including academic and nonacademic orthopaedic surgeons, male and female providers, and surgeons from different graduation years. DISCUSSION: Most of the results were attributed to third-party websites demonstrating that orthopaedic surgeons do not have notable control over their digital footprint. Increased patient visibility of physician-controlled websites and an objective rating system for patients remain potential areas of growth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".