242. RA Awareness Week: Increasing the Media Profile of Rheumatoid Arthritis
Bibliographic record
Abstract
Background: In 2013, we surveyed our members to find out what we should highlight in our second national awareness campaign and we were inundated with requests to raise awareness that RA is an invisible disease. They told us that the pain, fatigue and stiffness they live with cannot be seen by people around them and that family/friends didn’t fully understand what they live with on a daily basis. They also felt that RA isn’t talked about enough in the media and that people have misconceptions about the disease. Thus, our theme for our second RA Awareness Week (16–22 June 2014) became The Invisible Disease. Our key message was Let’s be vocal so RA is visible. Methods: To improve on 2013’s activity, we knew involvement from our members, supporters and followers on social media was key. Again, we hosted a thunderclap (a crowdspeaking platform that rallies people together to spread a message) which reached 200 000 people (120 000 more than in 2013) and used the hashtag #LookDeeper to generate discussions. We ensured that there were plenty of ways to get involved covering all abilities. We held our first national fundraising event, a walk entitled The RAmble. Our report, Invisible Disease: RA and Chronic Fatigue 2014, launched the week and was responsible for a great deal of national coverage, with fundraisers and groups securing considerable local coverage.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".