Real world data for rare diseases research: The beginner’s guide to registries
Bibliographic record
Abstract
Introduction Rare disease research has specific challenges that can be addressed using registries.Areas covered There are at least three different types of registries: patient registries, disease registries, and product registries. Patient registries recruit rare disease patients, potentially including several rare diseases within a registry, while disease registries can be considered a subset of patient registries which focus on specific diseases. Product registries focus on specific drugs. These registries may be used to conduct research that is specifically requested by a regulatory authority, they may be developed by a drug company to monitor the use of a particular drug or may be developed for public health monitoring purposes.Expert Opinion Compared to other sources of real-world data (RWD), such as electronic medical records (EMRs) and claims data, registries are more likely to have a correct diagnosis and more specific information about RDs. However, registries also have their challenges. Competition between registries may lead to missing or incomplete data. Registries could also have limited information on drug and medical history, which are better captured in EMRs or claims. Nevertheless, registries remain an important source of RWD in the rare disease space and are increasingly being leveraged to comply with regulatory requirements.
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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 | 0.123 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.048 | 0.053 |
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".