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Record W3003794993 · doi:10.1017/s0714980819000746

Cognistat: normes francophones pour les 60 ans et plus

2020· article· en· W3003794993 on OpenAlexaff
Martin Arguin, Joël Macoir, Carol Hudon

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité LavalUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsCognitionPsychologyPercentileDevelopmental psychologyPopulationDemographyPsychiatrySociologyStatistics

Abstract

fetched live from OpenAlex

Cognistat is a cognitive screening test that is widely used in English-speaking countries. Its French adaptation is now available. The present study aims to establish norms for a population aged 60 and over. One hundred and fifty-one participants aged between 60 and 84 years old with normal cognitive function were divided into 5 five-year age groups. The results on Cognistat are reported for each subtest and age group. Age has a significant effect in only two subtests (Attention and Language Comprehension), which suggests a reduced performance for older participants. However, these effects are very weak and irregular. For this reason and given data distribution, norms are proposed to define performance thresholds for the 15th (lower limit of the normal range), 10th (mild cognitive impairment) and 5th (clinically significant) percentiles for each subtest for the clinical use of Cognistat with individuals 60 years of age and older.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.820
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicDementia and Cognitive Impairment Research→French-language works237,207→