Influences of the Social Media Continuum Model in Radiology: Theory and Reality
Bibliographic record
Abstract
Social media utilization has been growing exponentially worldwide and has created a thriving venue for radiologists and the profession of radiology to engage in on both the academic and social levels. The aim of this article is to conduct updated literature review and address a gap in the literature by introducing a simple classification for social media utilization and a new theoretical model to outline the role and potential value of social media in the realm of radiology. We propose classifying social media through usage-driven and access-driven indices. Furthermore, we discuss the interdependency of radiologists, other physicians and non-physician stakeholders, scientific journals, conferences/meetings and the general public in an integrated social media continuum model. With the ongoing sub-specialization of radiology, social media helps mitigate the physical barriers of making connections with peers and audiences which would have otherwise been unfeasible. The constant evolution and diversification of social media platforms necessitates a novel approach to better understand its role through a radiological lens. With the looming fear of 'ancillary service' labelling, social media could be the golden plate to halt the path towards commoditization of radiology.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".