P126 Experience of 3D modelling in perianal fistula disease and survey of international surgical interest
Bibliographic record
Abstract
Background: Perianal Crohn's fistulas are often complex, translating to difficult surgery and worse outcomes. MRI is the reference standard for assessment of complex perianal fistula, being effective at defining anatomy and guiding surgery. [1] However, communication of radiologist's understanding of MRI findings to the surgeon remains challenging. 3D models can aid understanding of the complex relationship between sphincter anatomy and fistula, guiding surgical decisions and improving outcomes. We presented case vignettes where 3D reconstruction has improved surgical comprehension of complex disease and surveyed an international cohort of surgeons to assess current practice and to gauge interest in 3D modelling of perianal fistulas. Methods: During an international gastrointestinal conference, participants were asked to complete either an online or paper-based survey following presentation of perianal Crohn's fistula 3D models. Content validity of the questionnaire was performed using the IBD multi-disciplinary team and contained multi-category scales and binary (yes/no) questionnaires. Data collected included respondents' current fistula practice, their use of MRI in operative planning and whether they would be interested in 3D modelling in their institution. Figure 1. 2D vs. 3D complex perianal fistula. Results: Thirty-four participants completed the survey including 17 (50%) based in the UK, 4 (12%) from Denmark with the remaining participants from Canada, China, Finland, Germany, Greece, Spain, Saudi Arabia and Sri Lanka. Experience of perianal fistula surgery varied with 35% (12/34) having >10 years' experience and 30% and 35% having 5–10 and ≤5 years' experience respectively. 41% of the participants operate on complex perianal fistulas, whilst 65% also refer complex fistulas and 15% were from tertiary centres. The majority (88%) of surgeons review the patient's MRI pre-operatively with 24% using MRI peri-operatively and 44% using endoanal ultrasound. However, only 18% review the images with a radiologist all of the time, whilst 62% review 50–75% of the time and 12% never review the MRI with a radiologist. In total, 85% of respondents expect 3D modelling to be useful for perianal fistula surgery and 88% would use 3D modelling if available. Conclusions: There is an international appetite for use of 3D modelling of complex perianal Crohn's fistula, which supports our local experience. 3D reconstruction has potential to improve surgical understanding, improve surgical decision-making, augment training and aid patient comprehension for informed consent. References: [1] Szurowska E, Wypych J, Izycka-Swieszewska E, (2007), Perianal fistulas in Crohn's disease: MRI diagnosis and surgical planning: MRI in fistulazing perianal Crohn's disease, Abdom Imaging, 705–718, 32(6)
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".