Characteristics of General Surgery Social Media Influencers on Twitter
Bibliographic record
Abstract
Background The influence of social media and Twitter in general surgery research, mentorship, networking, and education is growing. Limited data exist regarding individuals who control the dialogue. Our goal was to characterize influencers leading the discussion in general surgery. Methods Right Relevance Insight API was searched for “general surgery,” and individual influencers were ranked by a comprehensive assessment of connections (followers/following) and engagement (likes, retweets, and comments). Profession, specialty, gender, and location were collected utilizing Twitter, Doximity, LinkedIn, ResearchGate, and institutional websites. American Board of Surgery and Royal College of Physicians and Surgeons of Canada were queried for board certification and academic h-index scores were acquired from Scopus. Results Eighty-eight individual influencers in general surgery were identified, with 73 holding positions in general surgery. Attending level general surgeons comprised 50%, of which 91% are board certified, and 94% completed a fellowship (surgical oncology, laparoscopic surgery, critical care/trauma, and colorectal surgery). Residents comprised 31%; 11% were nonsurgeons and 3% were not physicians. The majority of residents and fellow influencers were female (72%). Many general surgery influencers were international (51%), particularly Canadian (28% overall). The academic h-indices for these influencers (n = 73) ranged from 0 to 73 (mean 14.5 ± 8.2; median 9.5). Discussion Our data describe the positions, backgrounds, and research contributions of the top Twitter influencers in general surgery. Those engaged in social media should consider the background, expertise, and motivation of these influencers as the utilization and impact of this platform grows.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".