Advancements in the management of pediatric inflammatory bowel disease
Bibliographic record
Abstract
INTRODUCTION: The management of pediatric inflammatory bowel disease (PIBD) has drastically changed in the last decade. The limited availability of new biologics or small molecule therapies, and concerns about durability in children has necessitated the development of other advances in management to optimize care. AREAS COVERED: This review covers guidance for management targets and advances in optimizing biologic therapies, new medical therapies, adjuvant therapies, precision medicine and mental health concerns in PIBD. This review focused on recent advances and was not intended as a complete overview of the investigations and management of pediatric IBD. EXPERT OPINION: Advancements include standardization of treatment goals via a treat-to-target strategy, optimizing anti-TNF biologics through combination therapy or proactive drug monitoring, earlier initiation of treatment for Crohn's disease, the emergence of new biologic/advanced therapies and a growing focus on adjuvant therapies targeting the microbiome. Future progress relies on the inclusion of children/adolescents in clinical trials to facilitate faster regulatory approval for pediatric therapies and the integration of precision medicine and mental health screening to improve patient care and outcomes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".