Use of funded multicenter prospective longitudinal databases to inform clinical trials in rare diseases—Examination of cholestatic liver disease in Alagille syndrome
Bibliographic record
Abstract
The conduct of long-term conventional randomized clinical trials in rare diseases is very difficult, making evidenced-based drug development problematic. As a result, real-world data/evidence are being used more frequently to assess new therapeutic approaches in orphan diseases. In this investigation, inclusion and exclusion criteria from a published trial of maralixibat in Alagille syndrome (ALGS, ITCH NCT02057692) were applied to a prospective longitudinal cohort of children with cholestasis (LOGIC NCT00571272) to derive contextual comparator data for evolving clinical trials of intestinal bile acid transport inhibitors in ALGS. A natural history/clinical care cohort of 59 participants who met adapted inclusion and exclusion criteria of ITCH was identified from 252 LOGIC participants with ALGS with their native liver. Frequency weighting was used to match the age distribution of ITCH and yielded a cohort (Alagille Syndrome Natural History [ALGS NH]) that was very similar to the baseline status of ITCH participants. During a 2-year prospective follow-up there was a significant reduction in pruritus in the weighted ALGS NH cohort as assessed by the clinician scratch score (-1.43 [0.28] -1.99, -0.87; mean [SEM] 95% confidence interval). During the same time period, the total bilirubin, albumin, and alanine aminotransferase levels were unchanged, whereas platelet count dropped significantly (-65.2 [16.2] -98.3, -32.1). Weighted survival with native liver was 91% at 2 years in the ALGS NH. These investigations provide valuable real-world data that can serve as contextual comparators to current clinical trials, especially those without control populations, and highlight the value and importance of funded multicenter, prospective, natural history studies.
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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 | 0.170 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".