Practical Aspects of Recruitment and Retention in Clinical Trials of Rare Genetic Diseases: The Phenylketonuria (PKU) Experience
Bibliographic record
Abstract
Bringing treatments for rare genetic diseases to patients requires clinical research. Despite increasing activism from patient support and advocacy groups to increase access to clinical research studies, connecting rare disease patients with the clinical research opportunities that may help them has proven challenging. Chief among these challenges are the low incidence of these diseases resulting in a very small pool of known patients with a particular disease, difficulty of diagnosing rare genetic diseases, logistical issues such as long distances to the nearest treatment center, and substantial disease burden leading to loss of independence. Using clinical studies of phenylketonuria as an example, this paper discusses how, based on the authors' collective experience, partnership among clinicians, patients, study coordinators, genetic counselors, dietitians, industry, patient support groups, and families can help overcome the challenges of recruiting and retaining patients in rare disease clinical trials. We discuss specific methods of collaboration, communication, and education as part of a long-term effort to build a community committed to advancing the medical care of patients with rare genetic diseases. By talking to patients and families regularly about research initiatives and taking steps to make study participation as easy as possible, rare disease clinic staff can help ensure adequate study enrollment and successful study completion.
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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 | 0.208 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".