Comparing Cognitive Screening Tools in a Rural Ecuadorian Population: The Atahualpa Project (P5.215)
Bibliographic record
Abstract
OBJECTIVE: To compare two cognitive screening tools in a rural Ecuadorian sample and explore the influence of education on scores. BACKGROUND: Valid cutoffs for cognitive impairment and dementia using screening tools such as the Montreal Cognitive Assessment (MoCA) may not apply in rural samples with low education. Alternatives such as the Prueba Cognitiva de Leganés (PCL) have been validated and may be less influenced by education. DESIGN & METHODS: We did door-to-door interviews of all residents 蠅60 years in Atahualpa, Ecuador, assessing 1) socio-demographic and cardiovascular health (CVH) characteristics, and 2) cognition, using Spanish versions of the MoCA (range 0-30 points) and PCL (range 0-32 points). We examined the correlation between MoCA and PCL, the influence of education, and associations with characteristics, overall and excluding PCL-defined probable dementia cases (score <23; N=24). RESULTS: There were 274 participants with MoCA and PCL data (mean age 70±8; 59% women; education: 24% below primary, 57% primary, 19% beyond primary). MoCA (mean±SD=19±5; median=19, IQR=15-22) and PCL (mean±SD=27±3; median=27, IQR=25-29) scores were moderately correlated (R=0.4, p<0.0001), but differed (p<0.0001) for those with less than primary (mean ±SD MoCA=17±4, PCL=25±4), primary (MoCA=19±4, PCL=27±3), and beyond primary education (MoCA=21±4, PCL=27±3). Variables independently associated with MoCA scores in multivariable analyses included age, education, and male sex, while only age and education were associated with PCL scores. Adjusting for age, educational attainment explained a similar amount of variability on the MoCA (R-squared=13%) and the PCL (R-squared=8%), although education was less influential in both tests for those below the PCL dementia cutoff (MoCA R-squared=10%; PCL R-squared=6%). CONCLUSIONS: In this rural sample with prevalent low education, the MoCA and PCL were moderately correlated. Scores on both tests were influenced by education, but less so among those below the PCL cutoff for dementia. Refinement of screening tools to maximize validity in rural samples with low education are needed. Study Supported by: Universidad Espíritu Santo - Guayaquil, Ecuador. McKnight Brain Institute, U. Miami
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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 | 0.004 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".