The Role of Twitter in Radiology Medical Education and Research: A Review of Current Practices and Drawbacks
Bibliographic record
Abstract
The trends in society have provided favourable conditions for the rapid growth of radiology on social media, specifically there has been an expanding presence on Twitter. Currently, simple searches on Twitter yield a plethora of radiology education resources, that may be suited for medical students, residents or practicing radiologists. Educators have many tools at their disposal to deliver effective teaching. Over time, strategies such as including images and scrollable stacks often are more successful at gaining popularity or clicks online. Journals and authors can use Twitter to promote their new scientific work and potentially reach audiences they couldn't have prior. Attendees at conferences can get involved in the conversation by tweeting about the meeting and engaging with other attendees with mutual interests. Interested medical students, residents and even practicing radiologists can use Twitter as a means of networking and connecting with other scholars all around the globe. Within its glory, Twitter does carry some drawbacks including privacy concerns, equality, and risk of misinformation. Above all, the future of Twitter is bright and promising for all who are currently on it and plan to use it for their education, research, or professional advancement.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".