Cross‐cultural adaptation, validity and reliability of the Everyday Cognition Scale in Mexico M‐ECog in Older Adults with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer’s Dementia
Bibliographic record
Abstract
Abstract Background The Everyday Cognition (ECog) scale was created to assess the functional abilities of older adults across a wide range of abilities between normal aging and dementia. The original ECog was shown to have convergent and divergent external validity. Developed in 2008 by Farias et al and updated in 2021. This scale screens early cognitive alterations such as Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI) and Alzheimer’s dementia (AD) through the decline of basic activities and instruments of daily life (DLA)To establish validity, reliability, and cross‐cultural adaptation of the ECog in Spanish (M‐ECog) to identify: SCD, MCI, and Alzheimer’s‐type dementia in older Mexican adults. Method 200 patients and 200 informants in a memory clinic of a third level hospital in Mexico City. Four groups were formed: 50 cognitively healthy (CH), 50 SQD, 50 MCI and 50 AD. The clinical evaluation included: sociodemographic aspects, cognitive status by the Mini‐Mental State Evaluation (MMSE) and Montreal Cognitive Evaluation Spanish version (MoCA‐E). The informants rated the functional status measured through the KATZ and Lawton & Brody scales, as well as the ECog Spanish version (M‐ECog). Result The internal consistency of the overall function of the M‐ECog (Cronbach’s alpha) was 0.881. The intraclass correlation coefficient was 0.877 (95% CI, 0.850‐0.902; p<0.001). M‐ECog was significantly correlated with DLA 0.40 (95% CI, 0.320‐0.471; p<0.001), MMSE 0.68; (95% CI, 0.650‐0.710; p<0.001) and MoCA‐E 0.70 (95% CI, 0.620‐0.892; p<0.001). And it differentiated patients with SCD, MCI and AD from CH (p < 0.002). The area under the curve (AUC) for SQD was 0.70 (95% CI, 0.58‐0.82), p < 0.005 with a cut‐off value of 46 points, (Sensitivity (S) 99%, and Specificity (E) of 96%; the AUC for MCI was 0.94 (95% CI, 0.89‐0.99), p < 0.001 with a cut‐off point of 52 points, (S: 97%, E: 27%); and for the dementia group, the AUC was 0.86 (95% CI, 0.79‐0.92), p <0.001 with a cut‐off value of 85 points, with (S: 97%, E: 51%). Conclusion M‐ECog is a valid, reliable, useful instrument that measures daily skills mediated by cognition in older Mexican adults, self‐applicable (patients and informants) with SCD, MCI and Alzheimer’s Dementia
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".