A comparison of interventional clinical trials in rare versus non-rare diseases: an analysis of ClinicalTrials.gov
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To provide a comprehensive characterisation of rare disease clinical trials registered in ClinicalTrials.gov, and compare against characteristics of trials in non-rare diseases. DESIGN: Registry based study of ClinicalTrials.gov registration entries. METHODS: The ClinicalTrials.gov registry comprised 133,128 studies registered to September 27, 2012. By annotating medical subject heading descriptors to condition terms we could identify rare and non-rare disease trials. A total of 24,088 Interventional trials registered after January 1, 2006, conducted in the United States, Canada and/or the European Union were categorised as rare or non-rare. Characteristics of the respective trials were extracted and summarised with comparative statistics calculated where appropriate. MAIN OUTCOME MEASURES: Characteristics of interventional trials reported in the database categorised by rare and non-rare conditions to allow comparison. RESULTS: Of the 24,088 trials categorised 2,759 (11.5%) were classified as rare disease trials and 21,329 (88.5%) related to non-rare conditions. Despite the limitations of the database we found that rare disease trials differed to non-rare disease trials across all characteristics that we examined. Rare disease trials enrolled fewer participants (median 29 vs. 62), were more likely to be single arm (63.0% vs. 29.6%), non-randomised (64.5% vs. 36.1%) and open label (78.7% vs. 52.2%). A higher proportion of rare disease trials were terminated early (13.7% vs. 6.3%) and proportionally fewer rare disease studies were actively pursuing, or waiting to commence, enrolment (15.9% vs. 38.5%). CONCLUSION: Rare disease interventional trials differ from those in non-rare conditions with notable differences in enrolment, design, blinding and randomisation. However, clinical trials should aim to implement the highest trial design standards possible, regardless of whether diseases are rare or not.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 it