Catching fibrosis in motion: time-dependent diffusion MRI-derived cell diameter reveals the transitional state of intestinal scarring in Crohn’s disease
Bibliographic record
Abstract
Intestinal fibrosis represents a major long-term complication of Crohn’s disease (CD), arising from chronic tissue injury and marked by excessive deposition of extracellular matrix (ECM) components. This pathological remodeling leads to progressive luminal narrowing, strictures, and, ultimately, bowel obstruction.1 Notably, approximately one-third of individuals with CD will develop fibrotic complications, and among these, nearly half of patients will require surgical resection, highlighting the substantial clinical impact of fibrostenosing disease.2 The fibrogenic cascade in CD is driven primarily by activated mesenchymal cells—particularly fibroblasts and myofibroblasts—which respond to sustained inflammation by proliferating and secreting large quantities of ECM proteins, including collagen. This process results in irreversible structural alterations of the intestinal wall and contributes to disease progression.3 Despite the high prevalence of the fibrostenosing phenotype in CD, reliable noninvasive methods for diagnosing intestinal fibrosis remain limited and largely in a developmental stage. Differentiating fibrotic from inflammatory strictures remains a critical unmet need, as current tools often fail to capture the dynamic and heterogeneous nature of fibrotic remodeling. Among available imaging modalities, magnetic resonance imaging (MRI) has emerged as a key noninvasive tool due to its ability to provide detailed assessment of bowel wall morphology and composition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".