Clinician based decision tool to guide recommended interval between colonoscopies: development and evaluation pilot study
Bibliographic record
Abstract
BACKGROUND: Optimal intervals between repeat colonoscopies could improve patient outcomes and reduce costs. We evaluated: (a) concordance between clinician and guideline recommended colonoscopy screening intervals in Winnipeg, Manitoba, (b) clinician opinions about the utility of an electronic decision-making tool to aid in recommending screening intervals, and (c) the initial use of a decision-making smartphone/web-based application. METHODS: Clinician endoscopists and primary care providers participated in four focus groups (N = 22). We asked participating clinicians to evaluate up to 12 hypothetical scenarios and compared their recommended screening interval to those of North American guidelines. Fisher's exact tests were used to assess differences in agreement with guidelines. We developed a decision-making tool and evaluated it via a pilot study with 6 endoscopists. RESULT: 53% of clinicians made recommendations that agreed with guidelines in ≤ 50% of the hypothetical scenarios. Themes from focus groups included barriers to using a decision-making tool: extra time to use it, less confidence in the results of the tool over their own judgement, and having access to the information required by the tool (e.g., family history). Most were willing to try a tool if it was quick and easy to use. Endoscopists participating in the tool pilot study recommended screening intervals discordant with guidelines 35% of the time. When their recommendation differed from that of the tool, they usually endorsed their own over the guideline. CONCLUSIONS: Endoscopists are overconfident and inconsistent with applying guidelines in their polyp surveillance interval recommendations. Use of a decision tool may improve knowledge and application of guidelines. A change in practice may require that the tool be coupled with continuing education about evidence for improved outcomes if guidelines are followed.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".