K30 Lega Italiana Ricerca Huntington’s e malattie correlate (LIRH) foundation onlus: promoting research, with care
Bibliographic record
Abstract
Background LIRH Foundation is a not for profit organisation whose mission is to conduct and support research on Huntington’s disease (HD), providing patients with free of charge assistance, promoting awareness raising initiatives, training professionals and spreading research results. LIRH Foundation has outpatient services for HD in Northern, Middle and Southern Italy. Specific aim To encourage patients not to stay hidden but to seek for proper assistance by, at the same time, contributing to research progress. Methods A systematic, clear, easily accessible and scientifically validated communication and information activity conducted by press release, website, webmarketing campaigns, conferences, video, interviews, social networks, brochure, newsletter, networking. Results Incoming calls to our Toll-Free Number 800.388.330: 3.783 incoming calls in 2015 (20% increase vs 2014). 1.400 incoming calls in the first quarter of 2016. Website: 11.577 users (average) in the first quarter of 2016 vs 3.456 in the same period of 2015; 27.295 page views (average) in the first quarter of 2016 vs 13.135 same period 2015. Assisted HD subjects: Jul 2015-Jun 2016: 590 visits, including 72 first visits vs Jul 2014 – Jun 2016: 250 visits, including 25 first visits Conclusions Quality and consistency of disclosed information are recognised by patients, who feel encouraged to approach our organisation and to participate in experimental trials (i.e Enroll-HD, Legato-HD and Open PRIDE-HD).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.151 |
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".