Patients’ willingness to defer resection of diminutive polyps: results of a multicenter survey
Bibliographic record
Abstract
Abstract Background and study aims Current colonoscopy practice requires removal of diminutive polyps. This is associated with costs, but the benefits to colorectal cancer (CRC) prevention remain unclear. The study aim was to understand patients’ willingness to defer resection of diminutive polyps and to examine the factors that influence patients’ decisions. Patients and methods Adults presenting for a colonoscopy were surveyed at three hospitals in the USA and Canada. Survey domains included: patient characteristics, risk perception, knowledge about CRC risk, willingness to defer polyp resection, and associated concerns. The primary endpoint was the proportion of patients who would be willing to participate in a clinical trial that deferred resection of diminutive polyps. Secondary endpoints included factors associated with willingness to defer diminutive polyp resection. Results 557 eligible individuals completed the survey (mean age 63; 61 % men), with 50 % of respondents being willing to participate in a randomized trial in which resection of diminutive polyps would be deferred until the next surveillance colonoscopy (95 % confidence interval [CI] 46 % – 55 %). Outside of a clinical trial, 57 % of participants would be agreeable to deferring resection of diminutive polyps (95 %CI 51 % – 63 %). Willingness to defer diminutive polyp resection was associated with higher education (P = 0.001), greater knowledge about cancer risk (P = 0.002), and a lower perception of cancer risk (all P < 0.001). Age, sex, income, a history of polyps, and a first-degree family member with CRC were not associated with willingness to defer diminutive polyp resection. Conclusions More than half of individuals undergoing a routine colonoscopy would be agreeable to deferring resection of diminutive polyps and participating in a trial to evaluate this approach.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".