Is Transmural Healing an Achievable Goal in Inflammatory Bowel Disease?
Bibliographic record
Abstract
Background/Objectives: In the era of treat-to-target strategies in inflammatory bowel disease (IBD), transmural healing (TH) is gaining recognition as a promising therapeutic goal. TH has been associated with significantly better long-term outcomes, including reduced rates of hospitalization, surgery, and the need for therapy escalation. Cross-sectional imaging techniques, such as intestinal ultrasound (IUS), magnetic resonance imaging (MRI), and computed tomography enterography (CTE), offer a comprehensive, non-invasive means to assess this deeper level of healing. This review explores how TH is currently defined across various imaging modalities and evaluates the feasibility and cost-effectiveness of achieving TH with available therapies. Methods: A literature search was conducted across PubMed, Scopus, and Embase using keywords, including “transmural healing”, “intestinal ultrasonography”, “magnetic resonance imaging”, “computed tomography enterography”, “Crohn’s disease”, “ulcerative colitis”, and “inflammatory bowel disease”. Only English-language studies were considered. Results: Despite growing interest, there is no standardized definition of TH across imaging platforms. Among the modalities, IUS emerges as the most feasible and cost-effective tool, owing to its accessibility, accuracy (sensitivity 62–95.2%, specificity 61.5–100%), and real-time capabilities, though it does have limitations. Current advanced therapies induce TH in roughly 20–40% of patients, with no consistent differences observed between biologics and small molecules. However, TH has only been evaluated as a formal endpoint in a single randomized controlled trial to date. Conclusions: A unified and validated definition of transmural healing is critically needed to harmonize research and guide clinical decision-making. While TH holds promise as a meaningful treatment target linked to improved outcomes, existing therapies often fall short of achieving complete transmural resolution. Further studies are essential to clarify its role and optimize strategies for deep healing in IBD.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".