Subjective Cognitive Complaints and Cognitive Decline: Consequence or Predictor? The Epidemiology of Vascular Aging Study
Bibliographic record
Abstract
OBJECTIVES: To explore whether more cognitive complaints are associated with previous or future cognitive decline. DESIGN: Longitudinal; epidemiology vascular aging study. SETTING: Community in Nantes, France. PARTICIPANTS: Seven hundred thirty-three subjects, aged 59 to 71. MEASUREMENTS: Subjective cognitive complaints were recorded at 4-year follow-up examination in a prospective study of people aged 59 to 71 at study entry. Participants' cognitive performances were assessed repeatedly at each wave (baseline, 4 years, and 6 years) of the study using a series of neuropsychological tests including the Mini-Mental State Examination. Depressive symptoms were evaluated using the Center for Epidemiological Studies Depression Scale. Subjects also had a cerebral magnetic resonance imaging scan at 4-year follow-up to evaluate presence and severity of white matter hyperintensities (WMHs). RESULTS: Subjects with more cognitive complaints had greater cognitive decline. This significant relationship persisted after adjusting for potential confounders, including depressive symptoms. Multivariate analysis also showed that, in subjects without measured cognitive decline between study entry and 4-year follow-up, those with more cognitive complaints at 4-year follow-up had significantly greater measured cognitive decline during the subsequent 2 years. In the presence of severe WMH, more cognitive complaints were an even stronger predictor of future cognitive decline. CONCLUSION: Cognitive complaints reflect measured cognitive decline, but they also predict cognitive decline at an earlier stage than objective tests that are not able to detect cognitive deficits. They need to be taken into account in clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".