Do You See What I See? An Assessment of Endoscopic Lesions Recognition and Description by Gastroenterology Trainees and Staff Physicians
Bibliographic record
Abstract
Abstract Background Gastroenterologists should accurately describe endoscopic findings and integrate them into management plans. We aimed to determine if trainees and staff are describing inflammatory bowel disease (IBD) lesions in a similar manner. Methods Using 20 ileocolonoscopy images, participants described IBD inflammatory burden based on physician severity rating, and Mayo endoscopic score (MES) (ulcerative colitis [UC]) or simple endoscopic score (SES-CD) (Crohn’s disease [CD]). Images were selected based on agreement by three IBD experts. Findings of varying severity were presented; 10 images included a question about management. We examined inter-observer agreement among trainees and staff, compared trainees to staff, and determined accuracy of response comparing both groups to IBD experts. Results One hundred and twenty-nine staff and 47 trainees participated from across Canada. There was moderate inter-rater agreement using physician severity rating (κ = 0.53 UC and 0.52 CD for staff, κ = 0.51 UC and 0.43 CD for trainees). There was moderate inter-rater agreement for MES for staff and trainees (κ = 0.49 and 0.48, respectively), but fair agreement for SES-CD (κ = 0.37 and 0.32, respectively). For accuracy of response, the mean score was 68.7% for staff and 63.7% for trainees (P = 0.028). Both groups identified healed bowel or severe disease better than mild/moderate (P < 0.05). There was high accuracy for management, but staff scored higher than trainees for UC (P < 0.01). Conclusion Inter-rater agreement on description of IBD lesions was moderate at best. Staff and trainees more accurately describe healed and severe disease, and better describe lesions in UC than CD.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".