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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".