Concordance between the Mini-Mental State Examination, Short Portable Mental Status Questionnaire and Montreal Cognitive Assessment Tests for Screening for Cognitive Impairment in Older Adults
Bibliographic record
Abstract
Abstract Determine the level of concordance between the Mini-Mental State Examination (MMSE), Short Portable Mental State Examination (SPMSQ), and Montreal Cognitive Assessment (MoCA) screening test for cognitive impairment in older adults. A cross-sectional study based on an original cohort study. 1683 patients over 60 years-old were included between 2010 and 2015. Demographic information was collected and the MMSE, MoCA, and SPMSQ scores were obtained. Categorical variables were presented as frequencies and percentages, while numerical ones as median and interquartile range. The agreement was measured and adjusted by the number of years of education by Cohen’s Kappa index (k) with a 95% confidence interval (CI). The agreement was considered as good if k > 0.80. MMSE classified 43.32% of the patients as having cognitive impairment, MoCA 43.14%, and SPMSQ 24.84%. MMSE and MoCA showed an agreement (k) of 0.99 with a 95% CI of 0.99–1.00; MoCA and SPMSQ showed a k of 0.43 (95% CI: 0.38–0.46). Finally, MMSE and SPMSQ showed a k of 0.42 (95% CI: 0.37–0.46). The results did not change when performing the analysis by education subgroups. There was a strong concordance between MoCA and MMSE tests. Nevertheless, the SPMSQ was discordant with the other tests.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".