“I’m worth saving”‐ a qualitative study of people with dementia considering treatment with lecanemab
Bibliographic record
Abstract
Abstract Background As lecanemab becomes available to people living with dementia, there is a pressing need to understand how they weigh the potential benefits against costs. This study investigates how older adults perceive lecanemab’s risks and benefits and their approach to treatment decisions. Methods Semi‐structured interviews of older adults undergoing evaluation in Neurology clinics for lecanemab eligibility at two academic medical centers. An interdisciplinary research team used rapid thematic analysis guided by the Ottawa Decision Support Framework. Results 22 people completed interviews (mean age 70 years, 36% women, 100% white). Preliminary themes included: 1) Hopes, expected benefits, and the existential threat of dementia driving willingness and readiness to start lecanemab. Hopes included more time with family and more time feeling like themselves by stalling the progression of cognitive decline and amyloid build‐up. Some expected benefits included “getting back to where I was” and “getting better, not worse.” Some patients pursued Lecanemab because it would be doing ‘something’ (versus nothing), given the fear and stress of dementia. 2) Patients sought and obtained information from different sources, including advocacy organizations, the Internet, and clinicians. Patients desired more information about their personal risk and wanted to hear more from patients who took the medication. 3) Individual traits and preferences, family factors, and degree of trust in expertise influence how patients balance risks and benefits. Some patients were willing to accept treatment at any cost, either due to the perceived inevitability of decline without intervention or because they tend to “look past the negatives” when making decisions. Others weighed risks (e.g., brain bleeding) and financial and logistical costs carefully, but supportive families, insurance coverage, and trust in the system helped in their decision to start treatment. A small proportion of people would not get the treatment as the costs outweighed their personal benefit. Conclusions This group of people with mild dementia who are at the forefront of lecanemab treatment showed variation in hopes for treatment, information sought and obtained, and contextual factors in decision‐making, supporting the need for an individualized approach. These insights can guide future interventions to improve individualized decision‐making for lecanemab 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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".