Clinical Interview Assessment of Financial Capacity in Older Adults with Mild Cognitive Impairment and Alzheimer's Disease
Bibliographic record
Abstract
OBJECTIVES: To investigate financial capacity in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a clinician interview approach. DESIGN: Cross-sectional. SETTING: Tertiary care medical center. PARTICIPANTS: Healthy older adults (n=75) and patients with amnestic MCI (n=58), mild AD (n=97), and moderate AD (n=31). MEASUREMENTS: The investigators and five study physicians developed a conceptually based, semistructured clinical interview for evaluating seven core financial domains and overall financial capacity (Semi-Structured Clinical Interview for Financial Capacity; SCIFC). For each participant, a physician made capacity judgments (capable, marginally capable, or incapable) for each financial domain and for overall capacity. RESULTS: Study physicians made more than 11,000 capacity judgments across the study sample (N=261). Very good interrater agreement was obtained for the SCIFC judgments. Increasing proportions of marginal and incapable judgment ratings were associated with increasing disease severity across the four study groups. For overall financial capacity, 95% of physician judgments for older controls were rated as capable, compared with 82% for patients with MCI, 26% for patients with mild AD, and 4% for patients with moderate AD. CONCLUSION: Physicians and other clinicians can reliably evaluate financial capacity in cognitively impaired older adults using a relatively brief, semistructured clinical interview. Patients with MCI have mild impairment in financial capacity, those with mild AD have emerging global impairment, and those with moderate AD have advanced global impairment. Patients with MCI and their families should proactively engage in financial and legal planning, given these patients' risk of developing AD and accelerated loss of financial abilities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".