Social media in surgery: evolving role in research communication and beyond
Bibliographic record
Abstract
PURPOSE: To present social media (SoMe) platforms for surgeons, how these are used, with what impact, and their roles for research communication. METHODS: A narrative review based on a literature search regarding social media use, of studies and findings pertaining to surgical disciplines, and the authors' own experience. RESULTS: Several social networking platforms for surgeons are presented to the reader. The more frequently used, i.e., Twitter, is presented with details of opportunities, specific fora for communication, presenting tips for effective use, and also some caveats to use. Details of how the surgical community evolved through the use of the hashtag #SoMe4Surgery are presented. The impact on gender diversity in surgery through important hashtags (from #ILookLikeASurgeon to #MedBikini) is discussed. Practical tips on generating tweets and use of visual abstracts are presented, with influence on post-production distribution of journal articles through "tweetorials" and "tweetchats." Findings from seminal studies on SoMe and the impact on traditional metrics (regular citations) and alternative metrics (Altmetrics, including tweets, retweets, news outlet mentions) are presented. Some concerns on misuse and SoMe caveats are discussed. CONCLUSION: Over the last two decades, social media has had a huge impact on science dissemination, journal article discussions, and presentation of conference news. Immediate and real-time presentation of studies, articles, or presentations has flattened hierarchy for participation, debate, and engagement. Surgeons should learn how to use novel communication technology to advance the field and further professional and public interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".