B - 26 Examining Montreal Cognitive Assessment (MoCA) Performance by Primary and Testing Language
Bibliographic record
Abstract
Abstract Objective The MoCA has been translated into almost 100 languages including a Spanish version used in multiple validation studies. Research considering primary language and language of test administration is limited. We compared MoCA scores of primary Spanish speakers tested in English (S-E) with those of primary Spanish speakers tested in Spanish (S-S). Method This project involved secondary analysis of deidentified National Alzheimer’s Coordinating Center data. Cases with first visit MoCA data, demographics, and primary language of Spanish were included resulting in a final sample (N = 395) of S-S (n = 265) and S-E (n = 130) participants. Coarsened exact matching was used to derive a sample subset matched on age, education, and Clinical Dementia Rating global score (n = 160). Participants were compared on demographics and MoCA. Results In the final sample, S-S and S-E participants were not different in age, but educational level was lower in S-S (M = 10.0; SD = 5.3) than S-E (M = 14.6; SD = 3.9; p < 0.001) participants. MoCA scores were lower in S-S (M = 19.1, SD = 6.2) than S-E participants (M = 21.2, SD = 6.1; p = 0.002). A larger proportion of the S-S group (79%) had an abnormal MoCA compared to the S-E group (67%; p = 0.021). In the matched sample, MoCA scores were not significantly different between S-S (M = 21.2; SD = 4.8) and S-E (M = 21.2; SD = 5.3; p = 0.95) participants; rates of abnormal MoCA scores were not significantly different between S-S (73%) and S-E (66%; p = 0.39) participants. Conclusions Relationships between primary language, test administration language, and MoCA score were reduced in groups matched on education. Future research should explore additional education and language interactions among bilingual older 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".