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.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".