Implementation of Synoptic Reporting for Endoscopic Localization of Complex Colorectal Neoplasms
Bibliographic record
Abstract
Introduction Lack of documented tattooing of colorectal neoplasms at index colonoscopy results in high repeat preoperative colonoscopy rates. We developed national consensus recommendations for endoscopic localization and piloted an electronic synoptic reporting template. We report on the implementation and perceptions of using synoptic reporting to enhance colorectal lesion marking in a central Canadian healthcare system. Methods We implemented the template within our endoscopy reporting system and ran an infographic education campaign. We then conducted a follow-up email-based interview with all regional endoscopists. Thematic analysis and a mixed-methods triangulation approach were employed to synthesize qualitative and quantitative data. Results The interview was completed by 28/52 endoscopists (54%). Most (60.7%; n = 17) completed >100 colonoscopies and 71.4% (n = 20) identified six to 20 neoplasms requiring tattooing since introduction. A total of 50% (n = 14) used the template. Those not using it were unaware of it (42.9%; n = 12), or preferred using narrative text (17.9%; n = 5). Users reported modest mean functionality scores (intuitiveness: 3.56/5; efficiency: 3.7/5) and high impact scores (credible: 4.22/5; informative: 4.21/5). However, the perception of the synoptic template's ability to reduce the repeat preoperative colonoscopy rate was more circumspect (3.76/5). Conclusions Endoscopists believed the synoptic template was a functional, impactful tool that would improve communication and help to decrease the repeat preoperative colonoscopy rate. However, synoptic template uptake was limited by provider awareness, therefore more educational efforts are needed to increase uptake.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".