The Use of Twitter by Plastic Surgery Journals
Bibliographic record
Abstract
BACKGROUND: Social media have revolutionized the way we access information. Twitter is the most popular microblogging website and has become a tool for plastic surgery journals to connect with the greater academic community and public. The purpose of this study was to objectively assess the use of Twitter by plastic surgery journals. METHODS: Twelve plastic surgery journals were searched on Twitter. The following data were collected: age of Twitter profile, number of followers and tweets posted, and whether the journal's website had a link to Twitter or another social media website. All tweets were reviewed from May to July of 2017 inclusive, and the level of evidence of each original article posted in the tweets was recorded. Impact factor and Klout score (a social media influence score) were collected for all journals. RESULTS: Six of 12 plastic journals had a Twitter profile. The most social media-influencing journal in plastic surgery was Plastic and Reconstructive Surgery. This was followed by the Aesthetic Surgery Journal and the Journal of Hand Surgery (American and European Volumes). The presence of a Twitter profile was not associated with a higher impact factor for the journal. The Klout score was correlated with impact factor. Since joining Twitter, five of the six journals with Twitter profiles experienced increases in their impact factor. CONCLUSION: Twitter can be a quick and easy-to-use tool to increase exposure to evidence-based information from academic journals in plastic surgery.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".