Shared Decision-Making: Process for Design and Implementation of a Decision Aid for Patients With Craniosynostosis
Bibliographic record
Abstract
ObjectiveTo describe the process of developing a craniosynostosis decision aid.DesignWe conducted a mixed-methods exploratory study between August 2019 and March 2020 to develop a decision aid about surgical treatment for single suture craniosynostosis.SettingA single tertiary care academic children’s hospital.ParticipantsThe 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.InterventionsAfter 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).ResultsThree 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.ConclusionsWe 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 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 | 0.100 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".