Standardized Mini-Mental State Examination. Use and interpretation.
Bibliographic record
Abstract
OBJECTIVE: To review administration of the Standardized Mini-Mental State Examination (SMMSE) for dementia and depression and to evaluate how well it interprets older people's cognitive function. QUALITY OF EVIDENCE: Literature from January 1990 to December 1999 was searched via MEDLINE using the MeSH headings Alzheimer Disease, Vascular Dementia, Lewy Bodies, and Depression. Several studies have described the reliability and validity of the SMMSE. MAIN MESSAGE: The SMMSE, a standardized approach to scoring and interpreting older people's cognitive function, provides a global score of cognitive ability that correlates with daily function. Careful interpretation of results of the SMMSE, together with history and physical assessment, can assist in differential diagnosis of cognitive impairment resulting from Alzheimer's disease, vascular dementia, dementia with Lewy bodies, or depression. Repeated measurements can be used to assess change over time and response to treatment. CONCLUSION: The SMMSE is a valuable tool for family doctors who are often the first medical professionals to identify changes in patients' cognitive function. The SMMSE requires little time to complete and is a key component of a comprehensive dementia workup. Determining whether a patient has dementia is important because there are now effective medications that are most beneficial if started early.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.006 | 0.002 |
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".