Clinicians' Perspectives on How Disease Modifying Drugs for Alzheimer's Disease Impact Specialty Care
Bibliographic record
Abstract
Clinicians specialized in the diagnosis and management of persons living with early-stage Alzheimer's disease need to enable access, for those meeting criteria, to the new class of disease modifying drugs (DMDs). These drugs act on amyloid β42 and delay progression of symptoms. Thus, there will be interest from patients and families. Over the short term, the use of antibodies administered intravenously with serial MRIs to detect amyloid-related imaging abnormalities (ARIA) may require participation in structured phase 4 studies or in registries with third party funding for support staff and MRI scans. In the mid term, the availability of oral anti-amyloid therapy, likely with lower risk of ARIA, may transform clinical practice to a model of screening suitable patients using plasma biomarkers, with a subsequent rapid referral to a specialized memory clinic. Eventually, the biological profile of patients for amyloid, tau, and inflammation will determine which type of DMD to use. We are optimistic that clinicians will gain confidence with the use DMDs and answer the increasing needs of our aging population.
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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 | 0.046 | 0.098 |
| 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 | 0.009 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".