#Urology is trending in social media.
Bibliographic record
Abstract
Social media refers to Web-based applications through which users create and exchange user-generated content. Most social media networks are available free of charge to everyone who creates an account. Content is generated and shared in real time with users interacting through computers and mobile devices. These qualities make social media of unique utility to medicine: rapid exchange of ideas across the globe in one minute can impact patient care the next. Academic medicine is showing increased adoption of social media. Major medical journals (eg, British Medical Journal, The Lancet, New England Journal of Medicine, and the Journal of the American Medical Association) all have a presence on Facebook and Twitter. The peer-reviewed biographic database Scopus prominently displays social media impact in the sidebar of every article and abstract page.1 Urologists in particular have been quick to adopt social media for academic purposes. Matta and colleagues2 reported the dramatic increase in Twitter use at the American Urological Association (AUA) and Canadian Urological Association annual meetings. A combined 29 urologists generated 159 tweets at the 2012 meetings, compared with 268 urologists generating 2765 tweets in 2013. Several urology journals such as European Urology and the BJU International have embraced social media by creating Associate Editor roles for social media and digital media, respectively, and have actively encouraged the growth of social media among their readership. Two recent publications underscore the increasing use of social media in urology.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.027 |
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".