A-81 MoCA Cutoffs for English/ Spanish Bilingual Veterans Assessed in English
Bibliographic record
Abstract
Abstract Objective The Montreal Cognitive Assessment (MoCA) is a well-known screener of global cognitive functioning. Multiple studies have determined optimal cutoff scores for detection of cognitive impairment in various clinical populations. This study aims to determine appropriate cutoff scores in a clinically mixed bilingual (English/Spanish) sample. Methods A sample of n = 57 self-identified bilingual veterans referred for neuropsychological evaluation at a VA hospital completed the MoCA as part of a full battery. All tests were administered in English. The majority were male (96.4%), Hispanic/Latinx, with 14.65 mean years of education. Only MoCA total score without adding one point for ≤12 years of education was included. Descriptive statistics were used for sample characterization. ROC curve analysis assessed diagnostic accuracy of the MoCA for classification of cognitive impairment (CI). The CI group (n = 40) included both major or mild neurocognitive disorder. The nonimpaired group (n = 17) included persons with no CI or psychiatric diagnosis. Results ROC curve analysis was significant (p < .001) with an AUC of .857 (95% confidence interval .746-.969). A cutoff score of ≤24 was yielded an optimal balance of sensitivity (.900) and specificity (.706). Follow-up independent samples t-test and ANOVA were conducted to examine differences between groups. Conclusions Among bilingual individuals, a cutoff of ≤24 on the MoCA maximized sensitivity and specificity of accurately identifying cases of cognitive impairment. Findings have implications for identifying patients requiring further neuropsychological assessment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.003 | 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".