Chronic Granulomatous Disorder–Associated Colitis Can Be Accurately Evaluated with MRI Scans and Fecal Calprotectin Level
Bibliographic record
Abstract
Colitis is a common and serious complication of chronic granulomatous disorder (CGD) and requires assessment. Colonoscopy is invasive and carries risks of serious complication. We therefore assessed non-invasive monitoring via magnetic resonance imaging (MRI). We also evaluated fecal calprotectin (FCP), the Harvey-Bradshaw index (HBI) clinical score, and serum cytokines. We recruited 10 patients with CGD (8 males, mean age 29.6 years), scored a modified HBI, and obtained stool for FCP. The following day we took blood for cytokine measurement via Luminex, performed MR enterography (scored by two independent radiologists using three systems: London score, CDMI, and MaRIA) followed by colonoscopy with disease activity measurement via ulcerative colitis endoscopic index of severity (UCEIS). We assessed patient experience after each investigation and overall preference with follow-up questionnaires. MRI scores correlated well with colonoscopic gold standard (for London score R 2 0.91, p < 0.0001; for CDMI R 2 0.83, p = 0.0006; for MaRIA R 2 0.89, p = 0.0002). MRI was better tolerated and generally preferred, quicker, and visualized the entire large bowel whereas colonoscopy did not reach the terminal ileum in 3 participants. Elevated FCP accurately differentiated patients with colitis from those without, and log(calprotectin) correlated well with disease activity ( R 2 0.71, p = 0.009). Serum interleukin (IL)-12 concentration correlated with colitis activity but IL-1β and TNF did not. Harvey-Bradshaw index did not correlate with colitis activity. MRI and fecal calprotectin are useful methods for monitoring CGD colitis and should reduce the need for colonoscopy in these patients. IL-12 may represent an appropriate target for treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".