3 Tesla MRI- Crohn disease activity score: correlation with Crohn Disease Activity Index (CDAI), endoscopic findings and biological markers.
Bibliographic record
Abstract
Aim To work out a radiological score in the assessment of Crohn’s disease activity and severity. Materials and methods. From July 2011 to February 2012, 46 patients with suspected or established Crohn’s disease underwent small bowel MRI on a 3T scanner. According to radiological findings and disease behaviour phenotype (as proposed in the Montreal classification), patients were divided into 5 classes: absence of disease disease activity (presence of one of following findings: mucosal abnormalities, submucosal edema, mucosal enhancement) presence of substenosis without obstruction a. active disease b. inactive disease presence of stenosis with obstruction a. active disease b. inactive disease extramural involvement (fistulas and/or abscess) Data were correlated with endoscopical findings, CDAI, CRP and ESR. Results A significant correlation (r= 0.88, p< 0,001) was registered between endoscopical findings and MR score. A good correlation of MRI- CSI was observed with CDAI (r= 0,59, p<0,01); correlation was superimposable (r= 0,59, p<0,01) if subgroups were divided into active/ non active disease. A moderate correlation of MR- CSI was observed with ESR ( 0,49, p=0,001) and CRP (0,47, p=0,001). Correlation appears higher if subgroups were divided into active/ non active disease (0,66 and 0,59 respectively). Conclusion MR- CSI is a quick, manageable score, easy- to apply in daily practice; furthermore, MR can be used in the evaluation of CD as an alternative to ileocolonoscopy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".