Social Media in Neuro-Ophthalmology: Paradigms, Opportunities, and Strategies
Bibliographic record
Abstract
BACKGROUND: Social media (SoMe) is an integral part of life in the 21st century. Its potential for rapid dissemination and amplification of information offers opportunities for neuro-ophthalmologists to have an outsized voice to share expert-level knowledge with the public, other medical professionals, policymakers, and trainees. However, there are also potential pitfalls, because SoMe may spread incorrect or misleading information. Understanding and using SoMe enables neuro-ophthalmologists to influence and educate that would otherwise be limited by workforce shortages. EVIDENCE ACQUISITION: A PubMed search for the terms "social media" AND "neuro-ophthalmology," "social media" AND "ophthalmology," and "social media" AND "neurology" was performed. RESULTS: Seventy-two neurology articles, 70 ophthalmology articles, and 3 neuro-ophthalmology articles were analyzed. A large proportion of the articles were published in the last 3 years (2020, 2021, 2022). Most articles were analyses of SoMe content; other domains included engagement analysis such as Altmetric analysis, utilization survey, advisory opinion/commentary, literature review, and other. SoMe has been used in medicine to share and recruit for scientific research, medical education, advocacy, mentorship and medical professional networking, and branding, marketing, practice building, and influencing. The American Academy of Neurology, American Academy of Ophthalmology, and North American Neuro-Ophthalmology Society have developed guidelines on the use of SoMe. CONCLUSIONS: Neuro-ophthalmologists may benefit greatly from harnessing SoMe for the purposes of academics, advocacy, networking, and marketing. Regularly creating appropriate professional SoMe content can enable the neuro-ophthalmologist to make a global impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".