Self-reported word-finding complaints are associated with cerebrospinal fluid beta-amyloid and atrophy in cognitively normal older adults
Bibliographic record
Abstract
Abstract Background and Objectives Self-reported language complaints, and more specifically word-finding difficulties, are among the most frequent cognitive complaints in cognitively normal older adults (CN). The clinical significance of elevated self-reported word-finding complaints in CN is still a matter of debate. The present study aims at characterizing word-finding complaints in CN, establish their sociodemographic and psychological correlates, determine if they are predictive of lower levels of cerebrospinal fluid Aβ levels and finally, investigate if they are associated with brain atrophy in regions associated with naming impairments. Methods In this observational case-control study, 239 CN from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were selected. All participants completed the self-reported version of the Everyday Cognition (ECog) questionnaire, as well as a lumbar puncture for Aβ and a MRI. Results Word-finding complaints were rated equally severe as a few other memory items and significantly more severe compared to all the other cognitive complaints. Ecog-Lang1 (Forgetting the names of objects) was not related to any demographic (age, sex, years of education) or psychological variable (depression-related symptoms, anxiety-related symptoms), while Ecog-Lang3 (Finding the right words to use in a conversation) was significantly negatively associated with years of education and positively associated with depression-related symptoms. Ecog-Lang1 severity significantly predicted CSF Aβ levels in CN, and this result remained significant even when controlling for all demographic and psychological variables as well as general level of cognitive complaint. Individuals with high Ecog-Lang1 complaints showed atrophy in the left fusiform gyrus and the left rolandic operculum in comparison to CN with no or low Ecog-Lang1 complaints. Discussion Overall, our results support the fact that word-finding complaints are significant in CN and should be taken seriously. They have the potential to identify CN at risk of AD and support the need to include other cognitive domains in the investigation of subjective cognitive decline.
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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 | 0.001 | 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".