P1‐315: VALIDATION OF THE KOREAN‐EVERYDAY COGNITION (K‐ECOG)
Bibliographic record
Abstract
In the early diagnosis of dementia, an important factor is the evaluation of activities of daily living. The Everyday Cognition (ECog) scale was developed to measure functional changes that are the everyday correlates of specific neuropsychological impairments. This study aimed to examine the validity of the Korean version of Everyday Cognition (K-ECog). The participants were 268 cognitively normal older adults (NE), 151 amnestic mild cognitive impairment (aMCI), and 77 dementia of the Alzheimer's type (DAT). The Korean-Mini Mental State Examination (K-MMSE), Korean-Montreal Cognitive Assessment (K-MoCA), and Short form of the Geriatric Depression Scale were administered to all the participants. The K-ECog and Korean-Instrumental Activities of Daily Living (K-IADL) were rated by their informants. Internal consistency (Cronbach's α) of K-ECog Global Function was 0.93, and its test-retest reliability (Pearson's r) was 0.73. K-ECog was significantly correlated with K-IADL (.66), K-MMSE (−.38), and K-MoCA (−.26). Confirmatory factor analysis of K-ECog yielded a global and six domain-specific factors that the original ECog proposed. K-ECog Global Function and six domain-specific scores were significantly different across the NE, aMCI, and DAT groups (Table 1). Receiver Operating Characteristic (ROC) curve analyses showed that K-ECog effectively differentiated aMCI and DAT patients from NE, suggesting that K-ECog is as sensitive for detecting functional impairments as K-IADL. The proposed optimal cut-off score to differentiate aMCI from NE was 1.41.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".