Transperineal Ultrasonography in Perianal Crohn Disease: A Valuable Imaging Modality
Bibliographic record
Abstract
Aims of treatment for Crohn disease have moved beyond the resolution of clinical symptoms to objective end points including endoscopic and radiological normality. Regular re-evaluation of disease status to safely, readily and reliably detect the presence of inflammation and complications is paramount. Improvements in sonographic technology over recent years have facilitated a growing enthusiasm among radiologists and gastroenterologists in the use of ultrasound for the assessment of inflammatory bowel disease. Transabdominal intestinal ultrasound is accurate, affordable and safe for the assessment of luminal inflammation and complications in Crohn disease, and can be performed with or without the use of intravenous contrast enhancement. Perianal fistulizing disease is a common, complex and often treatment-refractory complication of Crohn disease, which requires regular radiological monitoring. Endoanal ultrasound is invasive, uncomfortable and yields limited assessment of the perineal region. Although magnetic resonance imaging of the pelvis is established, timely access may be a problem. Transperineal ultrasound has been described in small studies, and is an accurate, painless and cost-effective method for documenting perianal fluid collections, fistulas and sinus tracts. In the present article, the authors review the literature regarding perineal ultrasound for the assessment of perianal Crohn disease and use case examples to illustrate its clinical utility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".