P2–257: Validation of the Montreal Cognitive Assessment – Spanish Version test (MoCA‐S) as a screening tool for mild cognitive impairment and mild dementia in Bogotá, Colombia
Bibliographic record
Abstract
MoCA test was developed as a brief instrument to screen cognoscitive impairment. This is the first validation in Latin America of MoCA in Spanish (MoCA-S) developed in Colombia. Study of concordance by conformity to evaluated MoCA test in comparison with gold standard (Diagnostic in consensus of interdisciplinary assessment by Clinic of Memory). Assessment of psychometric properties of MoCA-S with interrater reliability, internal consistency and convergent validity. The internal consistency measure with α Cronbach index was 0,863 value similar to that obtained by the original designers of the MoCA test (Nasreddine et al 2005). The interrater reliability was almost perfect with a concordance coefficient of Lin=0.903 confidence interval (CI) = 95% (0,78–1.00) and the convergent validity was evaluated by means of the high correlation with the MMSE and the concordance with the gold standard of 85% patients perfectly classified by the test with a nomial kappa index of 0,7 (IC: 0,60–0.79).256 subjects were evaluated (64.4% women), of which 138 were patients and 118 healthy individuals. Patients were divided into two subgroups: MCI= 44 and MD = 94. Subjects with cognitive impairment averaged MoCA-S = 17.5 and MMSE = 25.3. The healthy group averaged MoCA-S = 25.39 and MMSE = 28.7. MCI subgroup averaged MoCA-S = 20.9 and MMSE = 27.5 (normal range). MD subgroup showed an average of MoCA-S = 15.4 (abnormal range) and MMSE = 24.4 (normal range). The difference of scores in the subgroups is statistically significant (p <0.0001). A sensitivity of 75% was established to detect MCI and of 94.4% for MD and the value of specificity was 79% with a cutting point ≥ 24. The relationship between the sensitivity and specificity was optimized with cutoff point of ≥24, was established a p ositive predictive value of 83% and a negative predictive value of 90%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".