Bibliographic record
Abstract
Orphan drugs are medicines or vaccines intended to treat, prevent or diagnose a rare disease (viz., Huntington’s disease, myoclonus disease, Tourette syndrome, etc.). The definition of rare diseases varies across jurisdictions but typically considers disease prevalence, severity, and existence of alternative therapeutic options. A rare disease is not universal and depends on the legislation and policies adopted by each region or country. In the last 35 years, ODA (Orphan Drug Act, 1983) has been adopted in several countries worldwide (USA, Australia, European Union, Japan, etc.) and has successfully promoted R and D investments to develop new pharmaceutical products for the treatment of rare diseases. The incidences of such diseases have been increasing at a greater pace than the speed with which drugs are researched and developed to treat such diseases. One of the major reasons is that the pharmaceutical industry is not very keen to research the development of orphan drugs as these drugs do not capture a bigger market. This is the current scenario in-spite of the various incentives provided in the orphan drug act. However, in this article, we have tried to focus on existing regulations and policies utilized by various countries namely USA, EU, Canada and Australia. It has been noted, most importantly that the two largest populated countries- China and India, both lack national legislation for orphan medicines and rare diseases, which could have substantial negative impacts on their patient populations with rare disease.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".