[IC‐P‐148]: LACK OF SELF‐AWARENESS OF COGNITIVE DEFICITS PREDICTS METABOLIC DECLINE IN MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Lack of awareness of cognitive decline is a common clinical feature of Mild Cognitive Impairment (MCI). However, the relationship between this lack of awareness and disease progression remain poorly understood. Here, we calculated change in brain metabolism in MCI subjects with different levels of self-awareness of cognitive deficits. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we defined low self-awareness based on the discrepancy between subject and caregiver global ratings on the Everyday Cognition (E-Cog) questionnaire. The cutoff as the threshold for low self-awareness was then calculated using the best operational point on the receiver operating characteristic (ROC) curve contrasting controls (n=332) and AD subjects (n=151). This cutoff then stratified 335 MCI subjects into high and low self-awareness groups. Simple linear regression models were used to evaluate the effect of awareness group on %difference between baseline and 24-month follow-up brain metabolism as assessed by [F]fluorodeoxyglucose ([F]FDG) positron emission tomography (PET). Here we show that MCI individuals with low self-awareness of cognitive deficits had decreased glucose metabolism at 24-month follow-up in the posterior cingulate cortex, bilateral medial temporal lobes, and basal forebrain compared to those with intact self-awareness.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".