An Asia Pacific Alliance for Rare Diseases
Bibliographic record
Abstract
Rare disease organizations representing small patient populations have had great impact in helping to secure orphan drug and rare disease legislation by joining forces, in the USA as a national alliance (National Organization for Rare Disorders) and in Europe as a regional alliance (European Organization for Rare Disorders), with a focus on affecting policy [ 1 – 3 ]. In the rest of the world, rare disease alliances have been slower to form and, for the most part, have had less visible impact on national policy. Notable exceptions are patient associations in Taiwan and Japan in the Asia-Pacific region and very recently the alliance in Colombia in Latin America. Through regional and international rare disease conferences and forums, rare disease associations have had opportunities to meet, to discuss common concerns, and to acknowledge their limited individual resources but potentially ‘significant’ collective capabilities. This paper reports on an initiative to establish an alliance of Asia-Pacific organizations, building on the efforts of national alliances and disease-specific groups in each country, with the collective goal of influencing rare disease policy and practice throughout the region.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".