5-Cog Study: Cross-Cultural Comparison of Subjective Cognitive Complaints in a Diverse Primary Care Population
Bibliographic record
Abstract
Abstract Subjective cognitive complaints (SCC) are risk factors for cognitive decline in older adults. A link between SCC and depressive symptoms has also been reported. These associations have not been much studied in non-White populations. We examined the relationship of SCC with cognitive function and depressive symptoms in adults aged 65 and older attending a primary care clinic in the Bronx. Five common SCC questions (four memory-related and one non-memory-related) were identified by literature review. Linear regressions, adjusted for age, sex and education years, were used to examine associations between individual SCC and cognitive function (Montreal Cognitive Assessment (MoCA) score and Hopkins Verbal Learning Test (HVLT) recall score) and depressive symptoms (Geriatric Depression Scale (GDS) score) for Hispanic (n=53) and non-Hispanic Black (n=47) adults. Mean number of SCC was similar for Blacks and Hispanics (2.3 vs. 2.4, p=0.752). Hispanics performed worse on the MoCA than Blacks (16.4 vs. 18.5, p=0.012), but education explained this difference. GDS and HVLT were similar across groups. For Hispanics only, a response of fair or poor to the question “how is your memory for a person your age?” was associated with worse MoCA scores (β -2.6; p=0.008). SCC were not associated with HVLT scores for either group. Four SCC for Blacks and two for Hispanics were associated with worse GDS scores. In an urban clinic population, SCC for Blacks and Hispanics were associated more with depressive symptoms than cognition. Further research is needed to identify SCC that better correlate with cognitive function in diverse populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".