EPID-15. GUIDELINES FOR THE OPTIMAL USE OF SOCIAL MEDIA FOR NEURO-ONCOLOGISTS
Bibliographic record
Abstract
Abstract BACKGROUND Approximately 80,000 new Americans are diagnosed with a brain tumor annually. Social media (SM) has emerged as a powerful tool for patient empowerment. Its effective utilization by healthcare professionals is vital for better connection to patients and dissemination of evidence-based information. OBJECTIVES: To develop guidelines for optimal utilization of SM by neuro-oncologists, through a review of user engagement with content posted on SM relating to brain tumors. METHODS Facebook (FB) and Twitter (TW) were searched using the term Brain Tumor. Page/account information such as user type, media (image/video) utilization in posts, and audience size (likes / followers) were recorded. Average activity and account annual growth (AAG) were calculated. Top qualitative themes were assigned. Correlations were assessed with Pearson’s test. RESULTS FB was predominantly used by organizations (64% of accounts) and provided a larger audience (67 pages,581 likes) than TW (25 accounts, 67,295 followers), which had similar proportion of personal (44%) and organizational accounts (52%). Charity & Fundraising (67%) was the top FB theme. Education & Research (72%) was the top TW theme. In FB, the page’s annual growth (PAG) was not related to rate of monthly posting or the length of activity on FB, but presence on other concurrent platforms (Rho: 0.59) was influential for PAG and audience size (p < 0.05). In TW, quantity of monthly tweets (Rho: 0.66) and media utilization (Rho: 0.78) significantly correlated with audience size and AAG (p-values < 0.05). CONCLUSION A multi-platform SM presence and use of relatable images/videos have a strong impact on both FB and TW engagement. FB is a stronger platform for organizations valuable for charity and fundraising. Multi-platform presence is more important than frequent posts on FB. TW is equally used by organizations and individuals, serving as an excellent medium for dissemination of research and educational material.
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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 | 0.080 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 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".