Designing Clinical Trials in Wilson’s Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Wilson's disease (WD) is an autosomal-recessive disorder caused by ATP7B gene mutations leading to pathological accumulation of copper in the liver and brain. Adoption of initial treatments for WD was based on empirical observations. These therapies are effective, but there are still unmet needs for which treatment modalities are being developed. An increase of therapeutical trials is anticipated. APPROACH AND RESULTS: The first Wilson Disease Aarhus Symposium (May 2019) included a workshop on randomized clinical trial design. The authors of the article were organizers or presented during this workshop, and this article presents their consensus on the design of clinical trials for WD, addressing trial population, treatment comparators, inclusion and exclusion criteria, and treatment endpoints. To achieve adequate recruitment of patients with this rare disorder, the study groups should include all clinical phenotypes and treatment-experienced as well as treatment-naïve patients. CONCLUSIONS: The primary study endpoint should be clinical or a composite endpoint until appropriate surrogate endpoints are validated. Standardization of clinical trials will permit pooling of data and allow for better treatment comparisons, as well as reduce the future numbers of patients needed per trial.
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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 | 0.461 | 0.551 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".