Montreal Cognitive Assessment’s auditory items (MoCA-22): Normative data and reliable change indices
Bibliographic record
Abstract
Our objective was to establish normative data and reliable change indices (RCI) for the Montreal Cognitive Assessment’s auditory items (MoCA-22). 4,935 cognitively unimpaired participants were administered the MoCA during an in-person visit to an Alzheimer’s Disease Research Center (Mage = 67.9, Meducation = 16.2, 65.8% women, 75.9% non-Hispanic-White), with 2,319 unimpaired participants returning for follow-up. Normative values and cutoffs were developed using demographic predictions from ordinary and quantile regression. Test-retest reliability was calculated using Spearman and intraclass correlations. RCI values were calculated using Chelune and colleagues’ (1993) formula. Education, age, and sex were all statistically related to MoCA-22 scores, with education having the strongest relationship. Notably, these relationships were not consistent across MoCA-22 quantiles, with education becoming more important and sex becoming less important for predicting low scores. These models were integrated into a calculator for deriving normative scores for an individual case. Furthermore, there was adequate-to-good test-retest reliability (ϱ = 0.56 95% CI [.54, .59]; ICC = 0.75, 95% CI [.73, .77]) and changes of at least 2-3 points are necessary to identify reliable change at 1-3-year follow-up. These findings add to the literature regarding utility of the MoCA-22 in the cognitive screening of older adults.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".