A Preliminary Comparison of Three Cognitive Screening Instruments in Long Term Care: The MMSE, SLUMS, and MoCA
Bibliographic record
Abstract
The Mini-Mental State Examination (MMSE) is a widely utilized cognitive screening instrument. Despite its popularity, there are problems with this instrument. Many researchers have questioned the utility of the MMSE when used among adults without cognitive impairment. Additionally, the MMSE lacks tasks targeting a wider variety of cognitive domains. Finally, the MMSE is no longer in the public domain and may be too costly for some settings. Given these problems, some mental health settings may be obliged to utilize another instrument, such as the Montreal Cognitive Assessment (MoCA) or the Saint Louis Mental Status Examination (SLUMS). The present pilot study examined the current literature related to the MoCA, SLUMS, and MMSE and compared performances on these measures across a sample of participants. A within-subject design was utilized to compare performance on the MMSE, MoCA, and SLUMS in a sample of 40 long-term care residents (aged 48–89). Several participants appeared to lack clinically significant cognitive deficits as assessed by the MMSE, but demonstrated clinically significant deficits as assessed by the MoCA or SLUMS. The MMSE was significantly positively correlated with both the MoCA (r = .90) and the SLUMS (r = .83). The results of this pilot study have important implications regarding how to choose an appropriate replacement for the MMSE for practitioners who utilize cognitive screening instruments.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".