Optimizing Timing of Follow-Up Colonoscopy: A Pilot Cluster Randomized Trial of a Knowledge Translation Tool
Bibliographic record
Abstract
BACKGROUND: Endoscopists have low adherence to guideline-recommended colonoscopy surveillance intervals. We performed a cluster-randomized single-blind pilot trial in Winnipeg, Canada, to assess the effectiveness of a newly developed digital application tool that computes guideline-recommended follow-up intervals. METHODS: Participant endoscopists were randomized to either receive access to the digital application (intervention group) or not receive access (control group). Pathology reports and final recommendations for colonoscopies performed in the 1-4 months before randomization and 3-7 months postrandomization were extracted. Generalized estimating equation models were used to determine whether the access to the digital application predicted guideline congruence. RESULTS: We included 15 endoscopists in the intervention group and 14 in the control group (of 42 eligible endoscopists in the city), with 343 patients undergoing colonoscopy before randomization and 311 postrandomization. Endoscopists who received the application made guideline-congruent recommendations 67.6% of the time before randomization and 76.1% of the time after randomization. Endoscopists in the control group made guideline-congruent recommendations 72.4% and 72.9% of the time before and after randomization, respectively. Endoscopists in the intervention group trended to have an increase in guideline adherence comparing postintervention with preintervention (odds ratio [OR]: 1.50, 95% confidence interval [CI] 0.82-2.74). By contrast, the control group had no change in guideline adherence (OR: 1.07, 95% CI 0.50-2.29). Endoscopists in the intervention group with less than median guideline congruence prerandomization had a significant increase in guideline-congruent recommendations postrandomization. DISCUSSION: An application that provides colonoscopy surveillance intervals may help endoscopists with guideline congruence, especially those with a lower preintervention congruence with guideline recommendations ( ClincialTrials.gov number, NCT04889352).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".