Uptake and barriers for implementation of the resect and discard strategy: an international survey
Bibliographic record
Abstract
Abstract Background and study aims Optical real-time diagnosis (= resect-and-discard strategy) is an alternative to histopathology for diminutive colorectal polyps. However, clinical adoption of this approach seems sparse. We were interested in evaluating potential clinical uptake and barriers for implementation of this approach. Methods We conducted an international survey using the “Google forms” platform. Nine endoscopy societies distributed the survey. Survey questions measured current clinical uptake and barriers for implementing the resect-and-discard strategy, perceived cancer risk associated with diminutive polyps and potential concerns with using CT-colonography as follow-up, as well as non-resection of diminutive polyps. Results Eight hundred and eight endoscopists participated in the survey. 84.2 % (95 % CI 81.6 %–86.7 %) of endoscopists are currently not using the resect-and-discard strategy and 59.9 % (95 % CI 56.5 %–63.2 %) do not believe that the resect-and-discard strategy is feasible for implementation in its current form. European (38.5 %) and Asian (45 %) endoscopists had the highest rates of resect-and-discard practice, while Canadian (13.8 %) and American (5.1 %) endoscopists had some of the lowest implementation rates. 80.3 % (95 % CI 77.5 %–83.0 %) of endoscopists believe that using the resect-and-discard strategy for diminutive polyps will not increase cancer risk. 48.4 % (95 % CI 45.0 %–51.9 %) of endoscopists believe that leaving diminutive polyps in place is associated with increased cancer risk. This proportion was slightly higher (54.7 %; 95 % CI 53.6 %–60.4 %) when asked if current CT-colonography screening practice might increase cancer risks. Conclusion Clinical uptake of resect-and-discard is very low. Most endoscopists believe that resect-and-discard is not feasible for clinical implementation in its current form. The most important barriers for implementation are fear of making an incorrect diagnosis, assigning incorrect surveillance intervals and medico-legal consequences.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".