Performance of a Short Version of the Everyday Cognition Scale (ECog-12) to Detect Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: The Everyday Cognition (ECog) 12-item scale, a functional decline measurement, can distinguish dementia from cognitively unimpaired (CU). Limited data compare ECog-12 performance by raters (self vs. informant) and scoring systems (average numeric vs. categorical grouping) to differentiate cognitive statuses. OBJECTIVES: To evaluate the performance of ECog-12 in differentiation cognitive statuses. DESIGN: A cross-sectional diagnostic test study. SETTING AND PARTICIPANTS: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study are analyzed. Participants were aged 55-90 years old divided into subgroups based on diagnostic criteria. MEASUREMENTS: We evaluated ECog-12 performance across different diagnostic groups, such as CU vs cognitive impairment (CI; mild cognitive impairment (MCI), and dementia), and the association between ECog-12 and CI. This procedure was repeated for self- and partner (informant)-reports. Additionally, types of ECog scores were also assessed, where an average ECog score was calculated (continuous numeric) as well as a categorical grouping ("any occasional declined" or "any consistently declined") based on item-level responses to ECog questions. RESULTS: ECog-12 cut-off scores of 1.36 (self-reported) and 1.45 (partner-reported) distinguish CU from CI with AUC 0.7 and 0.78, respectively. Adding a memory-concern question improved self-reported-ECog AUC to 0.79. Self- and partner-reported "consistently-declined" ECog-12 categorical grouping provided AUC 0.69 and 0.78. The study partner reported ECog-12 showed a greater association with CI than self-reported, with odds ratios of 35.45 and 8.79, respectively. CONCLUSION: Study partner-reported ECog scores performed better than self-reported ECog-12 in differentiating cognitive statuses, and a higher study partner reported ECog score was a higher prognostic risk for CI. A memory concern question could enhance self-reported ECog-12 performance. This further emphasizes the need to obtain data from study partners for research and clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".