The Impact of Social Media for Hand Surgeons: A Prevalence and Correlation Study With Online and Academic Reputations
Bibliographic record
Abstract
Purpose This study examines the influence of social media use among orthopedic and plastic-trained hand surgeons on patient-reported ratings online and academic productivity. Methods The American Society of Surgery of the Hand directory was queried for actively practicing orthopedic and plastic surgeons with a hand surgery fellowship. Each name was searched on various social media platforms. 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 20% of social media users in each cohort. Results A total of 97 orthopedic and 102 plastic surgeons were included. Overall, plastic surgeons were more active on social media compared to orthopedic surgeons. There was a positive association between having active profiles and Healthgrades ratings. When looking within the subgroups, the top 20% of social media orthopedic users were found to have a significantly higher mean Healthgrades rating and a mean number of comments than the rest of the cohort. On Vitals, the top 20% of social media users had higher mean ratings compared to the remaining 80%. The top 20% of plastics social media users had a significantly higher average Healthgrades rating compared to the rest of the plastics group. On Google reviews, the top 20% also had higher mean ratings, as well as mean number of ratings, compared to the rest of the cohort. Plastic surgeons with a Twitter/X account had a significantly higher h-index than plastic surgeons without a Twitter/X account (14.5 vs 9.2, P < .05). Conclusions Social media involvement is positively associated with surgeon ratings and the number of reviews and comments on physician rating websites. Using web-based marketing tools is still rare in hand surgery, especially among orthopedic surgeons. Type of study/level of evidence Economic/decision analysis IV.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".