A journey of hope: lessons learned from studies on rare diseases and orphan drugs
Bibliographic record
Abstract
Rare diseases are frequently life-threatening or chronically debilitating and the impact on the quality of life of affected patients and their family members is thus significant. However, drug development for these conditions has been limited by a lack of understanding of the underlying mechanisms of disease and the relative unavailability of subjects for clinical trials, as well as the prohibitive cost of investing in a novel pharmaceutical agent with poor market potential. Nevertheless, the introduction of Orphan Drug legislations has provided important incentives for the development of orphan drugs (i.e. drugs that have been abandoned or 'orphaned' by major drug companies). Moreover, recent studies on rare diseases, including inherited immunodeficiencies and metabolic disorders, have served not only to alleviate the plight of patients with rare diseases, but also yielded valuable information on biological processes of relevance for other, more common conditions. These lessons, along with the crucial importance of cooperation between academic institutions, pharmaceutical companies, patient advocacy groups and society in the elucidation of rare diseases, are highlighted in the present review.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".