A Simple Ultrasound Score for the Accurate Detection of Inflammatory Activity in Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Ultrasound is accurate in the detection of Crohn's disease. Our aim was to identify ultrasound parameters contributing to inflammatory disease activity, develop a simple score, and validate this score prospectively. METHODS: This study comprised 2 single-center investigations. The first was a retrospective study on a population that had received colonoscopies (as a gold-standard diagnostic) within 60 days of ultrasound. The second was a prospective study on 2 populations: patients requiring induction with adalimumab and patients on adalimumab maintenance therapy. Ultrasound and endoscopy were preformed within 14 days in both prospective groups. The endoscopy results were graded with the Simple Endoscopic Score and the Rutgeerts score and compared with 5 ultrasound parameters. We used a proportional odds model to determine which ultrasound parameters correlated significantly with the endoscopy results. We then developed a predictive ultrasound score for disease activity, plotted the receiver operating characteristic curves, and undertook prospective validation of the score. RESULTS: We evaluated 160 patients retrospectively to compare ultrasound and colonoscopy. Two of 5 parameters were found to correlate significantly with disease activity: bowel wall thickness (P = <0.0001) and color Doppler signal (P = 0.0292). We developed a score that uses weighted variables. The area under the corresponding receiver operating characteristic curve was 0.8658. CONCLUSIONS: A simple ultrasonographic score that accurately identifies Crohn's disease activity has been developed and validated. Ultrasound may be a surrogate for endoscopy to guide disease management, but future studies should be conducted to establish interrater variability.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".