Why Would Caregivers Not Want to Treat Their Relative's Alzheimer's Disease?
Bibliographic record
Abstract
OBJECTIVES: To determine family caregivers' willingness to use Alzheimer's disease (AD)-slowing medicines and to examine the relationships between this willingness, dementia severity, and caregiver characteristics. DESIGN: Cross-sectional survey. SETTING: In-home interviews of patients from the Memory Disorders Clinic of the University of Pennsylvania's Alzheimer's Disease Center. PARTICIPANTS: One hundred two caregivers of patients with mild to severe AD who were registered at an Alzheimer's disease center. MEASUREMENTS: Subjects participated in an in-home interview to assess their willingness to use a risk-free AD-slowing medicine and a medicine with 3% annual risk of gastrointestinal bleeding. RESULTS: Half of the patients had severe dementia (n=52). Seventeen (17%) of the caregivers did not want their relative to take a risk-free medicine that could slow AD. Half (n=52) did not want their relative to take an AD-slowing medicine that had a 3% annual risk of gastrointestinal bleeding. Caregivers who were more likely to forgo risk-free treatment of AD were older (odds ratio (OR)=1.7, P=.04), were depressed (OR=3.66, P=.03), had relatives living in a nursing home (OR=3.6, P=.02), had relatives with more-severe dementia according to the Mini-Mental State Examination (MMSE) (OR=2.29, P=.03) or Dementia Severity Rating Scale (DSRS) (OR=2.55, P=.002), and rated their relatives' quality of life (QOL) poorly on a single-item global rating (OR=0.25, P=.001) and the 13-item quality-of-life (QOL)-AD scale (OR=0.38, P=.002). Caregivers who were more likely to forgo a risky treatment were nonwhite (OR=6.53, P=.005), had financial burden (OR=2.93, P=.02), and rated their relative's QOL poorly on a single-item global rating (OR=0.61, P=.01) and the QOL-AD (OR=0.56, P=.01). CONCLUSION: These results suggest that caregivers are generally willing to slow the progression of their relative's dementia even into the severe stage of the disease, especially if it can be done without risk to the patient. Clinical trials and practice guidelines should recognize that a caregiver's assessment of patient QOL and the factors that influence it affect a caregiver's willingness to use AD-slowing treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".