Pediatric Crohn Disease Clinical Outcome Assessments and Biomarkers
Bibliographic record
Abstract
OBJECTIVE: There is a pressing need for drug development in pediatric Crohn disease (CD). Our aim was to provide strategic approaches toward harmonization of current thinking about clinical outcome assessments (COAs) and biomarkers to facilitate drug development in pediatric CD. METHODS: Scientists from the United States Food and Drug Administration, European Medicines Agency, Health Canada, and the Pharmaceuticals and Medical Devices Agency of Japan had monthly teleconferences from January 2014 through May 2015. A literature review was conducted to assess the measurement properties of all existing COA tools and to evaluate the current landscape of biomarkers used in pediatric CD. Based on the findings of literature review, we reached the consensus on the strategic approaches for evaluating outcomes in pediatric CD trials. RESULTS: The pediatric Crohn's Disease Activity Index, Crohn's Disease Activity Index, and Harvey-Bradshaw's index were used in pediatric CD clinical studies. But they lack adequate measurement properties (validity, reliability, and ability to detect change of the treatment) that are required to support approval of products intended to treat pediatric CD. Biomarkers (ie, fecal lactoferrin, osteoprotegerin, and calprotectin) have shown some promise for their potential as noninvasive surrogate endpoints in CD. CONCLUSIONS: Lack of well-defined and reliable COAs presents a hurdle for global drug development in pediatric CD. It is essential to develop well-defined and reliable COAs that can measure meaningful clinical benefit for patients in terms of how they feel, function, and survive. Development of noninvasive biomarkers as reliable surrogate endpoints needs to be further explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".