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Record W3035625530 · doi:10.1080/23279095.2020.1774885

Assessment of semantic memory in mild cognitive impairment: The psychometric properties of a novel semantic battery

2020· article· en· W3035625530 on OpenAlexaff
Avery Ohman, Christine Sheppard, Laura Monetta, Vanessa Taler

Bibliographic record

VenueApplied Neuropsychology Adult · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité LavalUniversity of OttawaBruyère
Fundersnot available
KeywordsPsychologyReliability (semiconductor)Battery (electricity)Semantic memoryCognitionInternal consistencyFace validityCognitive psychologyAudiologyClinical psychologyPsychometricsMedicinePsychiatry

Abstract

fetched live from OpenAlex

Semantic memory is stable in healthy older adults but shows decline in mild cognitive impairment (MCI). Current measures of semantic function do not assess multiple aspects of semantic function and/or are time-consuming to administer. Here we report the psychometric properties of a battery to detect semantic impairment that we recently developed and published. Study 1 determined the face validity of the battery; interviews were conducted with five professionals with expertise in MCI and language. Face validity interviews suggested the battery appropriately assesses semantic impairments. Study 2 assessed convergent validity and reliability (inter-rater reliability, test-retest reliability, and internal consistency). Participants included 102 healthy older adults and 60 people with MCI who completed a four-task semantic battery. Results demonstrate that performance on the semantic battery correlates with traditional measures of semantic function, inter-rater reliability and internal consistency was high, and there was no significant change in mean scores between participants' first and second testing sessions. The present findings suggest that the semantic battery is a reliable and valid assessment of semantic function. It is currently recommended for research use only.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.328
Teacher spread0.282 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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