Designing clinical trials in paediatric inflammatory bowel diseases: a PIBDnet commentary
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The optimal trial design for assessing novel therapies in paediatric IBD (PIBD) is a subject of intense ongoing global discussions and debate among the different stakeholders. However, there is a consensus that the current situation in which most medications used in children with IBD are prescribed as off-label without sufficient paediatric data is unacceptable. Shortening the time lag between adult and paediatric approval of drugs is of the upmost importance. In this position paper we aimed to provide guidance from the global clinical research network (Pediatric Inflammatory Bowel Disease Network, PIBDnet) for designing clinical trials in PIBD in order to facilitate drug approval for children. METHODS: A writing group has been established by PIBDnet and topics were assigned to different members. After an iterative process of revisions among the writing group and one face-to-face meeting, all statements have reached consensus of >80% as defined a priori. Next, all core members of PIBDnet voted on the statements, reaching consensus of >80% on all statements. Comments from the members were incorporated in the text. RESULTS: The commentary includes 18 statements for guiding data extrapolation from adults, eligibility criteria to PIBD trials, use of placebo, dosing, endpoints and recommendations for feasible trials. Controversial issues have been highlighted in the text. CONCLUSION: The viewpoints expressed in this paper could assist planning clinical trials in PIBD which are both of high quality and ethical, while remaining pragmatic.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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