MétaCan
Menu
Back to cohort
Record W2152192218 · doi:10.1177/1533317514534761

Correlational Analysis of 5 Commonly Used Measures of Cognitive Functioning and Mental Status

2014· article· en· W2152192218 on OpenAlexaboutno aff
Todd M. Solomon, Guy DeBros, Andrew E. Budson, Nikola Mirkovic, Cynthia Murphy, Paul R. Solomon

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaCognitionPsychologyCognitive impairmentClinical psychologyMontreal Cognitive AssessmentScale (ratio)Memory clinicMini–Mental State ExaminationAlzheimer's diseaseActivities of daily livingDiseasePsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

There are numerous measures for detecting the presence of dementia and quantifying its severity and progression. We analyzed the relations between scores on 5 commonly used measures (Mini-Mental State Examination, Montreal Cognitive Assessment, Alzheimer's Disease Assessment Scale-Cognitive Subscale, Activities of Daily Living Scale, and Global Deterioration Scale) of 101 successive admissions to a memory clinic. Patients were included in the analysis only if they received a diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease (AD) pathophysiological process or probable AD and if they received all measures. Regression analysis yielded 20 linear equations that allow for conversion between test scores on any 2 measures. Further, participants were grouped by MMSE scores with regard to level of disease severity, allowing for the creation of a quick reference table for estimating an approximate score range between measures. Results from this study provide a useful tool for clinicians when comparing between multiple different instruments that measure the mental status and functional ability of individuals with AD and MCI due to AD pathology.

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)0.000
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 score0.036
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.312
Teacher spread0.286 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Alzheimer s Disease & Other Dementias®Same topicDementia and Cognitive Impairment ResearchFrench-language works237,207