Shared Decision-Making: Process for Design and Implementation of a Decision Aid for Patients With Craniosynostosis
Bibliographic record
Abstract
<i>Objective</i>To describe the process of developing a craniosynostosis decision aid.<i>Design</i>We conducted a mixed-methods exploratory study between August 2019 and March 2020 to develop a decision aid about surgical treatment for single suture craniosynostosis.<i>Setting</i>A single tertiary care academic children’s hospital.<i>Participants</i>The decision aid development team consisted of surgeons, research fellows, a clinical nurse practitioner, clinical researchers with expertise in decision science, and a university-affiliated design school. Qualitative interviews (N = 5) were performed with families, clinicians (N = 2), and a helmeting orthotist to provide feedback on decision aid content, format, and usability.<i>Interventions</i>After cycles of revisions and iterations, 3 related decision aids were designed and approved by the marketing arm of our institution. Distinct booklets were created to enable focused discussion of treatment options for the 3 major types of single suture craniosynostosis (sagittal, metopic, unicoronal).<i>Results</i>Three decision aids representing the 3 most common forms of single suture craniosynostosis were developed. Clinicians found the decision aids could help facilitate discussions about families’ treatment preferences, goals, and concerns.<i>Conclusions</i>We developed a customizable decision aid for single suture craniosynostosis treatment options. This tool lays the foundation for shared decision-making by assessing family preferences and providing clear, concise, and credible information regarding surgical treatment. Future research can evaluate this tool’s impact on patient–clinician discussions about families’ goals and preferences for treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".