#PlasticSurgery
Bibliographic record
Abstract
BACKGROUND: Social media use is growing inexorably, and there is public appetite for evidence-based information. Little is known about engagement by plastic surgeons with social media. The aim of this study was to examine posting about plastic surgery on Twitter, to best inform how board-certified plastic surgeons could use the hashtag #PlasticSurgery as a tool to educate patients and the public. METHODS: A prospective analysis of 2880 "tweets" containing the words "plastic surgery" was performed. The following were assessed: identity of author, use of the hashtag #PlasticSurgery, subject matter, whether link to study was provided, and whether posts by surgeons were self-promotional or educational. RESULTS: Social media posting about plastic surgery is dominated by the public, accounting for 70.6 percent of posts versus only 6.0 percent by plastic surgeons. Only 5.4 percent of all tweets contained the hashtag #PlasticSurgery, although almost half of those that did were by plastic surgeons. Of these, 61.3 percent of posts by plastic surgeons were about aesthetic surgery; additional posts were about basic science, patient safety, and reconstruction (13.9, 4.0, and 2.3 percent, respectively). Eighteen scientific articles were referenced, with a link to the Journal site posted in two tweets. Of posts by plastic surgeons, 37.0 percent were self-promotional. CONCLUSIONS: The American Society of Plastic Surgeons and its Journal have recognized that social media may be used to educate and engage. Board-certified plastic surgeons have a great opportunity to promote evidence-based plastic practice by means of #PlasticSurgery in the interests of supporting patients and the profession.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.234 | 0.115 |
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".