Decision Aid for Colectomy in Recurrent Diverticulitis: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Diverticular disease is a common gastrointestinal diagnosis with over 2.7 million clinic visits yearly. National guidelines from the American Society of Colon and Rectal Surgeons state that "the decision to recommend elective sigmoid colectomy after recovery from uncomplicated acute diverticulitis should be individualized." However, tools to individualize this decision are lacking. OBJECTIVE: This study aimed to develop an online educational decision aid (DA) to facilitate effective surgeon and patient communication about treatment options for recurrent left-sided diverticulitis. METHODS: We used a modified design sprint methodology to create a prototype DA. We engaged a multidisciplinary team and adapted elements from the Ottawa Personal Decision Guide. We then iteratively refined the prototype by conducting a mixed methods assessment of content and usability testing, involving cognitive interviews with patients and surgeons. The findings informed the refinement of the DA. Further testing included an in-clinic feasibility review. RESULTS: Over a 4-day in-person rapid design sprint, including patients, surgeons, and health communication experts, we developed a prototype of a diverticulitis DA, comprising an interactive website and handout with 3 discrete sections. The first section contains education about diverticulitis and treatment options. The second section clarifies the potential risks and benefits of both clinical treatment options (medical management vs colectomy). The third section invites patients to participate in a value clarification exercise. After navigating the DA, the patient prints a synopsis that they bring to their clinic appointment, which serves as a guide for shared decision-making. CONCLUSIONS: Design sprint methodology, emphasizing stakeholder co-design and complemented by extensive user testing, is an effective and efficient strategy to create a DA for patients living with recurrent diverticulitis facing critical treatment decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".