Development of a Decision Aid for Patients With Low‐Risk Thyroid Cancer: A Mixed‐Methods Analysis of Feedback From Both Patient and Clinicians
Bibliographic record
Abstract
INTRODUCTION: Guideline-driven de-escalation of the extent of surgery for low-risk thyroid cancer has made treatment decisions more complex. Shared decision-making (SDM) is more involved than informed consent, improves patient satisfaction, and is considered standard of care. Patient decision aids (DA) can facilitate SDM but appropriate resources are lacking. METHODS: DA development occurred in 3 main phases. First, a prototype DA was developed and refined by a working group (clinicians, behavioral scientists, nurses, and trained consumer). Nationwide clinician consultation sessions obtained mixed-methods feedback leading to a hybrid paper-web DA ready for patient testing. Second, the paper DA was used within clinically appropriate consultations (Bethesda 3-6 thyroid nodules) and patient feedback obtained with the Ottawa acceptability and decisional conflict scales. Three cycles of iterative changes were made to the DA. Patient focus groups led to further refinements. Third, 40 clinicians were invited to review DA materials, providing mixed-methods feedback. RESULTS: Initial clinician consultation sessions (n = 113) revealed that surgeons used information resources more frequently in, and were more satisfied with, their current patient discussions around thyroid cancer management compared with endocrinologists (88% vs. 32% and 95% vs. 46% respectively, p < 0.01 for both). 95% of clinicians were open to using the DA, but concerns regarding availability, appropriateness, flexibility, credibility and potential to lengthen consultations, were raised. Patients reported that the DA was useful (97% paper, 100% web) and sufficient (85% paper, 100% web). Decisional conflict was low (17 paper vs. 12 web). Qualitative feedback led to changes to improve visual appeal, readability and minimize emotive responses. Clinician review of DA (60% response) reported no bias (73% paper, 79% web) and 86% felt the DA would be easily incorporated into practice. CONCLUSION: We present a hybrid paper and web-DA ready for wider testing in patients with low-risk thyroid cancer to complement SDM regarding the extent of surgery.
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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 | 0.002 |
| 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".