0943 Sleep Medicine Tweet-by-Tweet, an Electronic Platform for Collaborative Medical Education
Bibliographic record
Abstract
Abstract Introduction Twitter is a novel and accessible platform for the dissemination of medical education, and it is used by many medical practitioners (1). Many physicians have used Twitter as a means of meeting continuing medical education needs. This can manifest as Twitter-based Journal Clubs, curated conference data and webinars (2). Methods I have created a Sleep Medicine Medical Education Twitter account @SleepyNeuroDoc to share complex cases in all areas of sleep medicine, including sleep-disordered breathing, movement disorders in sleep, circadian rhythm disorders and nocturnal epilepsy. I share notable images of polysomnogram outputs, home sleep apnea tests, compliance data, neuro-imaging, electroencephalogram, cardiopulmonary coupling and more. This digital education platform allows rapid circulation of unique cases and promotes in-depth scholarly discussion, with no geographical limit. Polls are conducted for complex topics to facilitate knowledge exchange and consumer engagement. This educational twitter is followed by the entire spectrum of professions within the sleep medicine care team, including physicians, allied health, and researchers. To date, there are 40 cases posted. Results We conducted online questionnaires with consumers of this Twitter account, and the results so far indicate greater practitioner comfort with management of various sleep medicine conditions. Some consumers report having changed their approach to practice. Conclusion Our work suggests that this unique use of a social medical platform is beneficial for continuing medical education and knowledge exchange in the field of Sleep Medicine. References: 1. Chretien KC, Azar J, Kind T. Physicians on Twitter. JAMA. 2011;305(6):566-568. 2. Thamman R, Gulati M, Narang A, et al. Twitter-based learning for continuing medical education? Eur Heart J. 2020;41(46):4376-4379. Support (if any)
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.024 |
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".