Cardiovascular Conferences and Social Media
Bibliographic record
Abstract
Nearly 5 billion people use social media. 1 The use of social media platforms by physicians for professional use has changed the landscape of communication in medicine with rapid delivery of and access to information.It played and continues to play a role in accelerating innovation, expanding diversity, equity, and inclusion efforts, raising under-addressed topics, and propelling collaborations.Social media use has been accelerated by the COVID-19 pandemic and the evolution of virtual meetings.2 One of the most prominent social media platforms in medicine is Twitter, a microblogging site with 300 to 400 million users and characterized by 280-character messages, or "tweets." 1 One in 8 researchers use Twitter to varying extents.3 Twitter has been strategically leveraged in medicine to generate professional networks, discuss developments in different fields, and crowdsource solutions for commonly faced challenges.It has also been used to amplify research, such as covering high-impact clinical trials and
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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.038 | 0.034 |
| Insufficient payload (model declined to judge) | 0.054 | 0.026 |
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".