Design of a Non-Interventional Study to Assess Neurologists’ Perspectives and Pharmacological Treatment Decisions in Early Alzheimer's Disease
Bibliographic record
Abstract
INTRODUCTION: The current therapeutic landscape of Alzheimer's disease (AD) is evolving rapidly. Our treatment options include new anti-amyloid-β protein disease-modifying therapies (DMTs) that decrease cognitive decline in patients with early AD (prodromal and mild AD dementia). Despite these advances, we have limited information on how neurologists would apply the results of recent DMT trials to make treatment decisions. Our goal is to identify factors associated with the use of new AD DMTs among neurologists applying concepts from behavioral economics. METHODS: This non-interventional, cross-sectional, web-based study will assess 400 neurologists with expertise in AD from across Spain. Participants will start by completing demographic information, practice settings, and a behavioral battery to address their tolerance to uncertainty and risk preferences. Participants will then be presented with 10 simulated case scenarios or vignettes of common encounters in patients with early AD to evaluate treatment initiation with anti-amyloid-β DMTs (e.g., aducanumab, lecanemab, etc.). The primary outcomes will be therapeutic inertia and suboptimal decisions. Discrete choice experiments will be used to determine the weight of factors influencing treatment choices. RESULTS: The results of this study will provide new insights into a better understanding of the most relevant factors associated with therapeutic decisions on the use of DMTs, assessing how neurologists handle uncertainty when making treatment choices, and identifying the prevalence of therapeutic inertia in the management of early AD.
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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".