Mini-Mental State Exam Performance of Older African Americans: Effect of Age, Gender, Education, Hypertension, Diabetes, and the Inclusion of Serial 7s Subtraction Versus "World" Backward on Score
Bibliographic record
Abstract
The Mini-Mental State Exam (MMSE) is a clinically ubiquitous yet incompletely standardized instrument. Though the test offers considerable examiner leeway, little data exist on the normative consequences of common administration variations. We sought to: (a) determine the effects of education, age, gender, health status, and a common administration variation (serial 7s subtraction vs. "world" spelled backward) on MMSE score within a minority sample, (b) provide normative data stratified on the most empirically relevant bases, and (c) briefly address item failure rates. African American citizens (N = 298) aged 55-87 living independently in the community were recruited by advertisement, community recruitment, and word of mouth. Total score with "world" spelled backward exceeded total score with serial 7s subtraction across all levels of education, replicating findings in Caucasian samples. Education is the primary source of variance on MMSE score, followed by age. In this cohort, women out-performed men when "world" spelled backward was included, but there was no gender effect when serial 7s subtraction was included in MMSE total score. To ensure an appropriate interpretation of MMSE scores, reports, whether clinical or in publications of research findings, should be explicit regarding the administration method. Stratified normative data are provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".