The impact of social media for shoulder surgeons: a prevalence and correlation study with online and academic presence
Bibliographic record
Abstract
Background: Social media use has grown exponentially in the past decade and has become a powerful tool for physicians. A strong online presence can play a vital role for surgeons in patient recruitment and education and in promoting new literature. This study examines the influence of social media use on patient-reported online ratings and academic productivity among shoulder and elbow and sports medicine fellowship-trained orthopedic shoulder surgeons. Methods: The American Shoulder and Elbow Surgeons directory was queried for all active members who completed either a shoulder and elbow or sports medicine fellowship in the United States. Each name was searched on Twitter/X, Instagram, LinkedIn, ResearchGate, Facebook, TikTok, and YouTube for professional accounts, and the number of followers was recorded for each. The presence of a practice group or personal website was also recorded. Average ratings, number of reviews, and number of comments were collected from Healthgrades, Google Reviews, and Vitals. H-index was searched on Scopus. A summated social media presence score was calculated to identify the top 15% of social media users in each cohort. Results: 134 shoulder and elbow and 97 sports medicine fellowship-trained orthopedic shoulder surgeons were included in this review. The top 15% of social media users consisted of 35 shoulder surgeons: 16 with a shoulder and elbow fellowship and 19 with a sports medicine fellowship. The top 15% of users completed fewer years of practice on average since fellowship compared to the bottom 85%. On Healthgrades, the top 15% possessed higher mean ratings, number of ratings, and number of comments compared to the rest of the cohort. Active Instagram users had higher mean ratings on Google Reviews compared to those without an active profile. Across all social media platforms, those with profiles were found to have either similar or higher patient engagement on Healthgrades, Google Reviews, and Vitals. Among sports medicine surgeons, active Twitter/X and Instagram usage was also associated with a higher h-index compared to nonusers. Discussion: Social media involvement and overall online presence is positively associated with surgeon ratings and number of reviews and comments on physician rating websites, including Healthgrades, Google Reviews, and Vitals. Increased activity on Twitter/X and Instagram is also associated with an increased h-index. Social media involvement using web-based marketing tools continues to emerge in shoulder surgery by offering surgeons an outlet to reach patients and promote literature.
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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 | 0.001 | 0.004 |
| 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".