A39 IMPACT OF AN ENDOSCOPIC OPTICAL DIAGNOSIS COURSE ON GASTROINTESTINAL NEOPLASM CHARACTERIZATION SKILLS AMONG TRAINEE ENDOSCOPISTS
Bibliographic record
Abstract
Abstract Background Real-time endoscopic optical diagnosis involves detailed visualization of the digestive mucosal and microvascular pattern, allowing for prediction of histology. This is advantageous as it can guide endoscopic resection decisions, surgical referrals and provide cost savings to histopathology. There is, however, no standardized or widely implemented curriculum for endoscopists or trainees to learn optical diagnosis skills. Aims To evaluate the impact of a course for the optical diagnosis of esophageal squamous cell carcinoma (ESCC), early gastric cancer (EGC), Barrett’s esophagus (BE), and colorectal polyps Methods 19 gastroenterology trainees were invited to the 2-day course in Kingston, Canada. Number of procedures previously performed was recorded. The impact of the course on lesion diagnosis accuracy was evaluated using a pre-test, immediate post-test, and delayed post-test administered 6 weeks after the course. A repeated measures ANOVA was performed to compare the mean score (%) between the three tests. Results 13 trainees completed all the tests (Table 1). Mean score % values for the overall tests and test scores for esophageal, gastric, and colon lesions were summarized on Table 1. The repeated measures ANOVA determined that the mean score % had a statistically significant difference between the three tests (F (2,24)=5.63, P=0.01). Pairwise comparisons revealed that there was a statistically significant increase in mean scores between pre- and post-tests (13.86 (95% confidence interval (CI) of 4.36 to 23.33), p<0.01) with no significant difference between post and delayed post-tests (8.46 (95% CI of -0.12 to 17.04), p>0.05) (Figure 1). The mean correct % from the delayed post-test was higher than the pre-test but there was no statistical significance (5.39 (95% CI of -3.72 to 14.49), p>0.05). Conclusions This novel optical diagnosis course improved trainee accuracy in diagnosing ESCC, EGC, BE and colorectal polyps. Learning retention was evident, with no significant score difference between immediate and delayed post-tests. This study was limited by the low sample size and low number of test items. Further investigation is required to validate the course for international trainees and explore its clinical transferability. Demography and Mean Score % Figure 1. Mean score %. (*) denotes p<0.05. Funding Agencies None
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".