Treatment Preferences of Physicians Treating Adult Patients with Attention-Deficit/Hyperactivity Disorder in the United States and Canada: A Discrete Choice Experiment
Bibliographic record
Abstract
INTRODUCTION: Factors influencing attention-deficit/hyperactivity disorder (ADHD) treatment preferences have been studied among patients but not physicians in the United States (US) and Canada. This study assessed treatment preferences of physicians treating adult patients with ADHD in both countries. METHODS: An online discrete choice experiment (DCE) was conducted (October 4-20, 2023) among physicians from Dynata's US and Canadian panel who treated adult patients with ADHD. Preference weights for efficacy (improvement in ADHD symptoms) and safety [risks of adverse events (AEs)] attributes were estimated using a conditional logistic regression model, and were used to calculate the willingness to trade-off and relative importance of the attributes. RESULTS: Among 510 US and 347 Canadian physicians (64.1% and 69.2% male, respectively), improvement in ADHD symptoms had a significant positive impact, and the risks of AEs (except the risk of feeling jittery in Canada) had a significant negative impact on physician preferences for ADHD treatments. US physicians were willing to tradeoff 0.44, 0.35, 0.20, 0.17, and 0.17 percentage points of improvement in ADHD symptoms to avoid a one-percentage-point risk of insomnia, nausea, feeling jittery, anxiety, and dry mouth, respectively; among Canadian physicians, these were 0.31, 0.21, 0.12, 0.20, and 0.07, respectively. The relative importance of the efficacy versus safety attributes (i.e., the risks of AEs included in the DCE taken together) was 45.5% versus 54.5% in the US and 56.3% versus 43.7% in Canada. CONCLUSION: Efficacy was the most important single attribute for physicians treating adult patients with ADHD in both the US and Canada; however, the risks of AEs taken together had greater relative importance than efficacy alone among US but not Canadian physicians. These findings highlight potential discrepancies in physician and patient preferences based on existing evidence and underscore the importance of shared decision-making, which may in turn increase patients' treatment satisfaction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".