Scaling Cognitive Domains of the Montreal Cognitive Assessment: An Analysis Using the Partial Credit Model
Bibliographic record
Abstract
The psychometric properties of the Montreal Cognitive Assessment (MoCA) were examined by using the Partial Credit Model. The study sample included 897 participants who were distributed into two main subgroups: (I) the clinical group (90 patients with Mild Cognitive Impairment, 90 patients with Alzheimer's disease, 33 patients with Frontotemporal Dementia, and 34 patients with Vascular dementia, whose diagnoses were previously established according to a consensus that was reached by a multidisciplinary team, based on the international criteria) and (II) the healthy group (composed of 650 cognitively healthy community dwellers). The results show (i) an overall good fit for both the items and the persons' values, (ii) high variability for the cognitive performance level of the cognitive domains (ranging between 1.90 and -3.35, where "Short-term Memory" was the most difficult item and "Spatial Orientation" was the easiest item) and between the subjects on the scale, (iii) high reliability for the estimation of the persons' values, (iv) good discriminant validity and high diagnostic utility, and (v) a minimal differential item functioning effect related to of pathology, gender, age, and educational level. MoCA and its cognitive domains are suitable measures to use for screening the cognitive status of cognitively healthy subjects and patients with cognitive impairment.
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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 | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".