P1‐143: The Montreal cognitive assessment (MoCA): Validation of alternate forms and new recommendations for education corrections
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a popular cognitive screening test designed to detect mild cognitive impairment (MCI) in older adults (Nasreddine et al., 2005). It was originally validated in a normative sample with a mean education of 13.3 years using a cutoff score of 26. Our objective was to obtain normative data in a sample with 12 years or less of education, and to develop alternate forms to facilitate its use in repeated testing. Ninety-six healthy participants without cognitive problems were tested on the original MoCA and a neuropsychological battery. This sample was combined with lower-education participants from the initial validation study. Fifty-five participants were excluded, mainly due to poor neuropsychological performance (score = 1.5 SD below the mean on 1+ measure). The final sample was 79participants. Two alternate MoCA forms were developed by replacing original items with new exemplars. The original and two new forms were administered one month apart in randomized order to 32normal elderly controls, 30 patients with MCI, and 21 patients with Alzheimer disease. 1. Revised education corrections of +1 point for 10-12 years of education (n = 52) and +2 points for4-9 years of education (n = 27) are suggested. Sensitivity for detecting MCI/AD were 90/100% and specificity 69.2%for 10-12 years of education, and 87.5/100% sensitivity, 74.1% specificity for4-9 years of education. 2. Repeated measures ANOVA on the three MoCA forms revealed the following: total scores for the NECs and AD's on all three versions were within 0.8 points of each other, with no significant differences between the versions. Total scores for the MCIs showed a significant but small 1.3point difference between MoCA 1 and MoCA3. The MoCA has lower specificity for detecting MCI in lower education samples, highlighting the challenge of cognitive screening in older adults with lower education but, overall, retains its excellent psychometric properties and sensitivity as a screening tool for MCI and mild AD. The three forms of the MoCA yield equivalent total scores and discriminate MCI patients from controls and AD patients. The three forms are suitable for situations requiring repeated cognitive testing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".