Subjective memory complaints in community dwelling healthy older people: the influence of brain and psychopathology
Bibliographic record
Abstract
OBJECTIVES: Subjective memory complaints (SMC) are common. We aimed to characterize the relationship between psychiatric illness and white matter disease to SMC in a sample of healthy older people. MEASUREMENTS: Cognitively normal subjects between 55 and 90 years had age-adjusted and education-adjusted Consortium to Establish a Registry for Alzheimer's disease (CERAD) scores ≤1.5 SD from standard mean. ApoE genotyping was performed using polymerase chain reaction. Sixty subjects (30 SMC, 30 controls) underwent 3T MRI, which was rated by two raters blinded to the diagnosis, for periventricular (PVH) and deep white matter hyperintensities (DWMH) using the Fazekas scale. Subjective memory was assessed by asking the participant, Do you feel like your memory or thinking is becoming worse? RESULTS: Two hundred and fifteen volunteers were assessed. Ninety-six were cognitively normal (mean age 62.5 years). SMC were reported by 52/96 subjects (54%). These were compared with subjects who denied SMC. Participants with a history of depression or anxiety were more likely to have SMC (p = 0.02). The frequency distribution of ApoE4 allele and CERAD scores were similar. White matter load was similar (p ≤ 0.47), with a high prevalence of PVH and DWMH seen (100% and 88% of scans, respectively). CONCLUSION: Both SMC and white matter disease were common. SMC were associated with a history of depression or anxiety but not with white matter disease. Evaluation for a history of depression and anxiety in people with SMC is supported by these findings.
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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 | 0.003 |
| 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 | 0.001 | 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 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".