Social media to enhance engagement and science dissemination during in-person and virtual medical conferences: the SCMR 2020 and 2021 experiences: a report of the SCMR social media task force
Bibliographic record
Abstract
Most cardiac imaging conferences have adopted social media as a means of disseminating conference highlights to a global audience well beyond the confines of the conference location. A deliberate and thoughtful social media campaign has the potential to increase the reach of the conference and allow for augmented engagement. The coronavirus disease 2019 (COVID-19) pandemic triggered a radical transformation in not just the delivery of healthcare but also the dissemination of science within the medical community. In the past, in-person medical conferences were an integral annual tradition for most medical professionals to stay up to date with the latest in the field. Social distancing requirements of the COVID-19 pandemic resulted in either cancelling medical conferences or shifting to a virtual format. Following suit, for the first time in its history, the 2021 Society for Cardiovascular Magnetic Resonance (SCMR) annual meeting was an all-virtual event. This called for a modified social media strategy which aimed to re-create the sociability of an in-person conference whilst also promoting global dissemination of the science being presented. This paper describes the employment of social media as well as the evolution through the SCMR scientific sessions for 2020 and 2021 that serves as a model for future cardiovascular conferences.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".