51 Importance of Using Baseline Verbal Abilities When Interpreting the MOCA Test Performance
Bibliographic record
Abstract
Objective: The Montreal Cognitive Assessment (MOCA) is widely used as a mental status screening test to detect cognitive impairment in adults over 55 years of age. Performance on this test ranges from 0 to 30. One point is given to individuals with 12 or lower years of education. This accommodation is based on the fact that low education may be a risk factor for dementia (Milani et al., 2018). However, studies suggest the one-point adjustment may not be sufficient to address the impact of low education on test performance (Malek-Ahmadi et al., 2015). The aim of this study is to compare the effects of educational achievement versus baseline verbal abilities on MOCA performance. Participants and Methods: Fifty patients (25 male; mean age=72.78, SD = 8.11; mean education=16.18, SD = 2.73) with cognitive concerns were referred to Massachusetts General Brigham. All underwent neuropsychological evaluation, including screening with the MOCA. Total MOCA scores were calculated. In this patient group, the MOCA scores ranged from 10 to 29 (mean=22, SD=5.129). Measures of literacy (Wechsler Test of Adult Reading or Test of Premorbid Functioning) were used to estimate baseline verbal abilities. Educational achievement was based on self-reported years of education. Results: Correlational analyses included the Total MOCA scores, measures of literacy, and years of education. Performance on the MOCA significantly correlated with measures of literacy, r(43)=.578, p< .001, and a stepwise regression analysis revealed that literacy predicted performance on the MOCA, R2=.041, F(3,139)= 9.172, p<.001. Years of education correlated with measures of literacy, r(44)=.494, p< .001, but not with performance on the MOCA. Conclusions: Findings suggest that education-adjusted scoring on the MOCA may not be sufficient to “level the playing field” in terms of MOCA performance. Years of education had less of an effect on the Total MOCA scores than did baseline verbal abilities. It may be the case that literacy has a more robust effect on MOCA performance due to the inherent verbal nature of the MOCA. Data from this study highlight the importance of considering a patient’s baseline verbal abilities in the interpretation of their MOCA performance.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".