A Useful and Brief Cognitive Assessment for Advanced Dementia in a Population with Low Levels of Education
Bibliographic record
Abstract
BACKGROUND/AIMS: Almost half of community-dwelling patients and 59.6% of institutionalized residents with dementia are in moderate or severe stages of this disease. The Mini-Mental State Examination (MMSE) has limited applicability to these patients due to floor effects. We aimed to determine the correlation between the MMSE and the Severe Mini-Mental State Examination (SMMSE), as well as SMMSE association with functional scales in patients having moderate to severe dementia and low levels of education. METHODS: A cross-sectional study of patients 60 years or older attending an outpatient clinic was conducted. The MMSE, SMMSE and functional scales were applied. Clinical and demographic data from medical records were reviewed. RESULTS: Seventy-five patients with a mean of 4.1±3.6 years of education were analyzed. The mean scores on the MMSE and SMMSE were 7.8±7.0 and 17.8±9.4, respectively. The results indicated that the MMSE and SMMSE correlated only in patients who had an MMSE score of less than 10 (r=0.87; p<0.001). In addition, significant correlations were found between the SMMSE and functional scales (p<0.001). It was observed that educational level did not interact with SMMSE performance. CONCLUSION: The SMMSE is a useful and reliable tool for a brief cognitive assessment of advanced dementia patients with low educational levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".