Sex Influences the Accuracy of Subjective Memory Complaint Reporting in Older Adults
Bibliographic record
Abstract
Subjective memory complaints (SMC) are required when diagnosing amnestic mild cognitive impairment (aMCI), although their relationship with objective memory performance and Alzheimer's disease (AD) pathology remains unclear. We investigated whether the sex of the patient/participant moderates these associations. Participants were 940 normal control (NC) and aMCI participants from the Alzheimer's Disease Neuroimaging Initiative. SMC were assessed via the memory scale of the Everyday Cognition questionnaire. Discrepancy scores were calculated between self- and informant-reports and categorized into "overestimates," "comparable estimates", and "underestimates" of SMC. We conducted linear and logistic regressions to examine the interaction of sex with self- and informant-reported SMC and discrepancy group on the Rey Auditory Verbal Learning Test (RAVLT) Immediate and Delayed Recall and on PET measures of amyloid-β (Aβ) positivity. Diagnosis-stratified analyses were also conducted. Overall, there were sex by self- and informant-reported SMC interactions for Immediate and Delayed Recall. Despite a higher proportion of "overestimates" in women, greater self- and informant-reported SMC showed a stronger relationship to poorer RAVLT scores in women versus men. Diagnosis-stratified analyses revealed that results were driven by aMCI participants. Conversely, overall, greater self- and informant-reported SMC related to greater odds of Aβ positivity regardless of sex. In diagnosis-stratified analyses, only informant-reported SMC related to Aβ positivity in aMCI. Relative to "comparable estimates," "underestimates" of SMC were associated with poorer RAVLT scores across sexes in the overall sample and in aMCI. The predictive utility of self-report SMC may be limited to women in aMCI. Sex differences should be considered when evaluating SMC.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".