P2‐394: Impact of Disclosing Amyloid Status on Caregivers: A Memory Clinic Study
Bibliographic record
Abstract
To this date, no authors have studied the impact of disclosing amyloid status on caregivers of patients with dementia in the real-life context of a memory clinic. In addition to the clinical utility of amyloid PET in differential diagnosis of atypical dementia, we aimed to explore the impact of disclosing amyloid status on caregivers. A total of 3,542 patients presenting to our clinic with cognitive deficits over a 2-year period were screened. Among them, 270 FDG-PET scans were conducted to clarify diagnosis beyond history, neurological examination, basic laboratory tests, neuropsychological assessment, and MRI. A total of 28 cases (mean age 59.3 yrs, SD 5.8; mean MMSE 21.4, SD 6.0) remained with no clear diagnosis. These patients underwent amyloid imaging. One month after disclosure of amyloid status, caregivers were asked to complete a 21-item Likert scale questionnaire along with a 1-hour interview designed to explore the impact of amyloid status disclosure. Our cohort was 50% amyloid positive. Amyloid PET was associated with a diagnostic change in 9/28 cases, a significant 44% increase in diagnostic confidence, and altered management in 20 cases. Caregiver impact was qualitatively highly favorable. Feelings of anxiety and depression were decreased. Disease perception, anticipation of the future, and quality of life following the scan were improved. Despite caregivers reacting favorably to the disclosure process, trends did not reach statistical significance partly because patients nonetheless remained affected by diagnosis of an irreversible degenerative disease and this dominated all caregivers' reactions. This preliminary effort to assess the impact of disclosing amyloid status on caregivers of patients with dementias suggests an overall positive role for this technique in the clinic. In addition to its utility in the differential diagnosis of atypical dementia, amyloid PET enhanced caregiver experience in several positive ways despite the burden of a devastating disease.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".