Screening for Mild Cognitive Impairment: Comparing the SMMSE and the ABCS
Bibliographic record
Abstract
OBJECTIVE: To compare the sensitivity and specificity of the AB Cognitive Screen (ABCS) with the Standardized Mini-Mental State Examination (SMMSE) to differentiate normal cognition from mild cognitive impairment (MCI), especially when educational level and age are taken into account. METHOD: This cross-sectional study took place at geriatric outpatient memory clinics. Participants were community-dwelling adults, aged 55 years or over, referred from primary care settings (a minority of participants were referred from specialists) for assessment of memory loss and age-matched control subjects with no complaint of memory loss. Each participant had the ABCS and the SMMSE administered in random order on the same day. RESULTS: Participants included 124 patients diagnosed with MCI and 111 with normal cognitive function. The ABCS showed a statistically significant difference between normal cognition and MCI (ABCS score 111.7 and 104.6 points, respectively, P < 0.001) for the whole group. This difference was significant with the ABCS, regardless of participants' age or education. There was a significant difference between normal cognition and MCI for SMMSE scores (SMMSE score 27.8 and 27.2 points, respectively, P = 0.040), but the differences were not significant when age and education were taken into account. Age and education were shown to affect the scores of both instruments except for the ABCS scores of MCI subjects, which were not significantly affected by education (P = 0.059). CONCLUSIONS: The ABCS is more sensitive than the SMMSE in differentiating normal cognition from MCI. The ABCS appears to be less influenced by education. It has improved clinical utility with a wider range of scoring gradations, reduced ceiling effects, and shorter scoring and administration times.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".