A-16 MoCA Subtest Analysis: Discriminating between Healthy Controls and MCI
Bibliographic record
Abstract
Abstract Objective The Montreal Cognitive Assessment (MoCA) is a suitable, sensitive, and specific cognitive screener for detecting mild cognitive impairment (MCI). Previous research has found markers to discriminate between healthy controls and MCI on MoCA subtest scores. Specifically, MCI performed worse on executive functioning and attention tasks (i.e., inverse digits, serial 7’s, repetition, fluency, abstraction, and word recall). The aim of the present study is to assess for discrimination patterns in MoCA performance between healthy controls and MCI. Method Data was collected through the National Alzheimer’s Coordinating Center (NACC). A sample of healthy controls (n = 3776, 65% female, 80% White, 17% Black, 3% Asian/Pacific Islander) and MCI (n = 1143; 51% female, 82% White, 15% Black, 3% Asian/Pacific Islander) were examined. Results An initial independent t-test revealed a statistically significant difference in MoCA scores for healthy controls (M = 26.18, SD = 2.78) and MCI (M = 22.01, SD = 3.49; t(4917) = 36.91, p = 0.000, Cohen’s d = 1.32). Additional t-tests were performed to compare MoCA subtest scores and domain scores for diagnostic groups. There was a statistically significant difference for healthy controls and MCI groups across all MoCA subtests and domains. Further examination using normal distribution revealed worse performance on cube copy and word recall in MCI groups. Conclusions Consistent with previous findings, word recall was able to discriminate between healthy controls and MCI. However, this study was able to find discrimination in cube copy performance. These findings may guide clinicians to use these interval changes as early cognitive markers for impairment, allowing for early detection and intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".