State of rare disease management in Southeast Asia
Bibliographic record
Abstract
BACKGROUND: Rare diseases, also referred to as orphan diseases, are characterised by their low prevalence with majority of them are chronically debilitating and life threatening. Given the low prevalence and the widely dispersed but very small patient base for each disease, there may often be a disproportion in the availability of treatments and resources to manage patients, spur research and train experts. This is especially true in Southeast Asian countries that are currently in the process of implementing or revising their universal health coverage schemes. This paper aims to examine the status of rare disease management in Southeast Asian countries. It will serve as the basis for a more active discussion on how countries in the region can address an under-recognised rare disease burden and enhance national and regional capacities. METHODS: The study consists of literature reviews and key stakeholders interviews in six focus countries, including the Philippines, Singapore, Malaysia, Indonesia, Vietnam, and Thailand and five countries as best practice, comprising of France, Canada, Australia, Taiwan, and South Korea. Rare disease management initiatives across each country were examined based on the World Health Organization's framework for action in strengthening health systems. RESULTS: The results suggest rare disease management remains challenging across Southeast Asia, as many of the focus countries face fundamental issues from basic healthcare systems to funding. Nonetheless, there are substantial improvement opportunities, including leveraging best practices from around the world and organising a multi-stakeholder and regional approach and strategy. CONCLUSIONS: Southeast Asian countries have made significant progress in the management of rare disease, but there remain key areas for substantial development opportunities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".