Clinical Utility of Amyloid PET Imaging in the Differential Diagnosis of Atypical Dementias and Its Impact on Caregivers
Bibliographic record
Abstract
Recent studies have supported a role for amyloid positron emission tomography (PET) imaging in distinguishing Alzheimer's disease (AD) pathology from other pathological protein accumulations leading to dementia. We investigated the clinical utility of amyloid PET in the differential diagnosis of atypical dementia cases and its impact on caregivers. Using the amyloid tracer 18F-NAV4694, we prospectively scanned 28 patients (mean age 59.3 y, s.d. 5.8; mean MMSE 21.4, s.d. 6.0) with an atypical dementia syndrome. Following a comprehensive diagnostic workup (i.e., history taking, neurological examination, blood tests, neuropsychological evaluation, MRI, and FDG-PET), no certain diagnosis could be arrived at. Amyloid PET was then conducted and classified as positive or negative. Attending physicians were asked to evaluate whether this result led to a change in diagnosis or altered management. They also reported their degree of confidence in the diagnosis. Caregivers were met after disclosure of amyloid PET results and completed a questionnaire/interview to assess the impact of the scan. Our cohort was evenly divided between positive (14/28) and negative (14/28) 18F-NAV4694 cases. Amyloid PET resulted in a diagnostic change in 9/28 cases (32.1%: 17.8% changed from AD to non-AD, 14.3% from non-AD to AD). There was a 44% increase in diagnostic confidence. Altered management occurred in 71.4% (20/28) of cases. Knowledge of amyloid status improved caregivers' outcomes in all domains (anxiety, depression, disease perception, future anticipation, and quality of life). This study suggests a useful additive role for amyloid PET in atypical cases with an unclear diagnosis beyond the extensive workup of a tertiary memory clinic. Amyloid PET increased diagnostic confidence and led to clinically significant alterations in management. The information gained from that test was well received by caregivers and encouraged spending quality time with their loved ones.
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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 | 0.001 | 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) | 0.001 | 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".