Current trends in biobanking for rare diseases: a review
Bibliographic record
Abstract
Abstract: Rare diseases (RD) refer to a collection of approximately 5,000–8,000 individual diseases that have a low prevalence and are often genetic in origin. While RD can manifest throughout life, they frequently affect children and newborns. Common characteristics include being severe, disabling, life-threatening, degenerative and affecting different organ systems. The burden of RD is often exacerbated by a lack of specific treatments. Whilst there is etiological heterogeneity, there is overlap in cellular and molecular pathways. Amongst specialists, there is legitimate hope that based on genetic knowledge and pathway definition, a new medical classification system, currently called “precision medicine”, will be developed, which may change our view on how to apply shared therapeutic targets. Thus, collection of clinical and genetic data and biospecimens (in biobanks) will play an increasing role in diagnoses and development of therapies for RD. Biobanks are maintained collaboratively by researchers or their institutions, and involve a delicate balance between health policy objectives, academic research, public good outcomes, and community trust. Due to the nature of RD, international cooperation is critical for sharing limited numbers of RD samples and achieving a critical mass. Here we review the current and future direction of RD biobanks and discuss research and development stemming from the use of biospecimens to improve management of RD. Keywords: biobank, biospecimen, rare disease Corrigendum for this paper has ben published
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".