Linking Cognitive Screening Tests in Community-Dwelling Older Adults: Crosswalk between the Montreal Cognitive Assessment-Basic and the Mini-Mental State Examination
Bibliographic record
Abstract
OBJECTIVE: To develop the crosswalk between the Montreal Cognitive Assessment-Basic (MoCA-B) and Mini-Mental Status Examination (MMSE) based on a community-dwelling older population to facilitate data synthesis and comparison. DESIGN: A cross-sectional study. SETTING AND PARTICIPANTS: We used baseline data of 2170 subjects with total MoCA-B and MMSE scores from an ongoing prospective cohort study, the Beijing Longitudinal Disability Survey in Community Elderly (BLINDSCE). METHODS: The MoCA-B and MMSE were administered by trained assessors. Equipercentile equating was used to develop the conversion table between MoCA-B and MMSE scores in the total sample and subgroups by age, sex, residency, and education level. The mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland-Altman plot were used to evaluate the linking performance. RESULTS: MoCA-B and MMSE scores converted bi-directionally for the overall sample and subgroups, with small standardized MAE (SMAE) and high ICC. The linking results between MoCA-B and MMSE scores were consistent across the total sample and the age and sex subgroups, while a 2-score difference was observed within the residency and education subgroups. CONCLUSIONS AND IMPLICATIONS: This study provides easy-to-use crosswalks between measures of MoCA-B and MMSE with precision among community-dwelling older adults. Our results help to compare and pool data across studies using either of the 2 cognitive screening tests and provide a useful reference to clinicians for better evidence-based practice in patients evaluated using different cognitive tests.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".