Measuring Cognition in a Geriatric Outpatient Clinic: Rasch Analysis of the Montreal Cognitive Assessment
Bibliographic record
Abstract
OBJECTIVE: To evaluate the psychometric properties of the Montreal Cognitive Assessment as a quantitative measure of cognitive ability. DATA ANALYZED: A total of 222 cases extracted from a clinical database (57-91 years) of patients screened for cognitive impairment in outpatient geriatric assessment clinics. DATA COLLECTED: Demographic information and individual item responses to Montreal Cognitive Assessment. RESULTS: Comparison of the data with a unidimensional Rasch model indicated that the total score obtained by summing across all items yields a reliable (0.75) quantitative estimate of global cognitive ability. All items fit the model and together spanned a range of difficulty from -3.75 to +2.88 logits. Items were assessed for differential item functioning across such patient characteristics as age, education, and language spoken. We provide a table for converting Montreal Cognitive Assessment total scores onto a linearly scaled score, with guidelines for interpreting changes in Montreal Cognitive Assessment score in terms of their statistical significance. CONCLUSIONS: The Montreal Cognitive Assessment can provide a reliable and valid quantitative estimate of cognitive ability in a geriatric cognitive disorders clinic setting.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".