Cross-cultural adaptation of the everyday cognition scale (M-ECog) in older Mexican adults with cognitive impairment
Bibliographic record
Abstract
The Everyday Cognition (ECog) scale was created to evaluate the functional abilities of older adults across a wide range of abilities between normal aging and dementia. ECog screens cognitive alterations such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI). This early recognition is done by the measurement of the ability to perform the activities of daily living (ADLs). Objective: To establish the cross-cultural adaptation, validity, and reliability of the ECog Mexican version (M-ECog) in participants with: SCD, MCI, and dementia coming from a memory clinic. Methods: There were 200 patients and their respective informants in a memory clinic of a third level hospital in Mexico City. Four groups were studied: 50 cognitively healthy (CH), 50 SCD, 50 MCI, and 50 dementia. The clinical evaluation included: sociodemographic and health characteristics, cognitive status by the Mini-Mental State Evaluation (MMSE) and Montreal Cognitive Evaluation Spanish version (MoCA-E), and caregiver information (informants) about the difficulty in ADLs as well as the ECog Spanish version (M-ECog). Results: The M-ECog was significantly correlated with MMSE, MoCA-E, and ADLs. It showed the ability to discriminate the different cognitive declines (Cronbach's alpha 0.881). The intra-class correlation coefficient was 0.877 (95% confidence interval - CI, 0.850-0.902; p<0.001). The patient's group area under curve (AUC) of M-ECog for SCD was 0.70 (95%CI 0.58-0.82, p<0.005), for MCI it was 0.94 (95%CI 0.89-0.99, p<0.001) and for dementia 0.86 (95%CI 0.79-0.92, p<0.001). Conclusion: The M-ECog scale proves to be valid and reliable for measuring everyday abilities mediated by cognition. It is self-applicable without requiring extensive prior formation. It is useful to screen for SCD and MCI in older Mexican adults.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".