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Record W2976805143 · doi:10.1080/23279095.2019.1668791

Expected performance of Quebec-French older adults on the <i>Batterie Rapide de Dénomination</i> (BARD)

2019· article· en· W2976805143 on OpenAlexaffabout
Joël Macoir, Carol Hudon

Bibliographic record

VenueApplied Neuropsychology Adult · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFlemishNeurocognitiveNominationTest (biology)MedicinePsychologyGerontologyCognitionPsychiatryHistory

Abstract

fetched live from OpenAlex

Difficulties retrieving words during conversations, called anomia, are frequent in the late preclinical stage of Alzheimer’s disease, in mild cognitive impairment, and in major neurocognitive disorders. Picture-naming tests, used to assess anomia, are too lengthy and are unsuitable for medical or nursing practices. The main objective of this study was to confirm the usefulness of the Batterie Rapide de Dénomination (BARD - Battery of Rapid Denomination), an electronically-administered picture-naming test comprising 10 pictures for which perfect naming scores were obtained in French, English and Flemish participants. In this study, the BARD was applied on 207 healthy, French-speaking participants from Quebec, aged 50 years and more, wherein the results demonstrated the ease of naming the 10 pictures of the test. However, their performance was not always flawless. Seven out of the 207 participants of produced one error on one specific picture, which led us to excluding this item from the BARD. Thus, this study established the utility of the BARD for clinical settings of French-Quebec populations. This screening test is ideally suited for bedside assessment in acute care settings, stroke units and medical environments providing primary and secondary care. Its use has the potential for improving referrals to more specialized resources.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.256
Teacher spread0.249 · 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.

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
Published2019
Admission routes2
Has abstractyes

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Same venueApplied Neuropsychology AdultSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207