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Record W2312837386 · doi:10.1093/arclin/acr054

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

2011· article· en· W2312837386 on OpenAlexaff
Keith A. Hawkins, Jennifer Cromer, Andrea S. Piotrowski, Godfrey D. Pearlson

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNormativeSubtractionMedicineInclusion (mineral)DemographyGerontologyPsychologySocial psychologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.381
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2011
Admission routes1
Has abstractyes

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