Challenges of the set-up process for academic led international studies of rare diseases
Bibliographic record
Abstract
Researchers and patients agree that rare diseases require new and better therapies. Trials in rare diseases have many challenges, relating to the scarcity of patients and experts, with the inevitable conclusion that large scale studies will require recruitment of patients from multiple centres in different countries. We describe the challenges of setting up an international (US, UK, Canada, Italy, Germany) trial of three steroid regimes in the management of children with Duchenne muscular dystrophy, funded by the US-based NIH(NINDS). Contract negotiations proved difficult. Differences in the definition of sponsorship between the US and the UK required a major discussion before resolution could be achieved. Risk aversion on behalf of all parties caused major delays. Concerns about exchange rate fluctuations meant that individual researchers were asked to bear the risk of an adverse shift in the exchange. Most of the 40 participating sites requested individual amendments to the model contract, in part because of a lack of compatibility with standard contracts commonly used in the different participating countries; recurrent issues included the currency to be used for payments and who bore responsibility for indemnification. The discrepancies in the interpretation of the EU Clinical Trials Directive across European countries and the differences between the regulatory and ethical approval processes in Europe, US and Canada meant that we had five separate and distinct timelines and frameworks to deal with in terms of getting regulatory, ethical and local approvals. A concerted approach is needed to resolve the specific challenges of setting up multicentre studies in rare diseases
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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 | 0.794 | 0.718 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.056 | 0.035 |
| Open science | 0.023 | 0.060 |
| Research integrity | 0.026 | 0.069 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".