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Record W2942305769 · doi:10.1684/nrp.2017.0438

Le POP-40 : un nouvel outil d’évaluation de la mémoire sémantique liée aux personnes célèbres

2018· article· fr· W2942305769 on OpenAlexaffabout
Sophie Benoit, Isabelle Rouleau, Roxane Langlois, Valérie Dostie, Sven Joubert

Bibliographic record

VenueRevue de neuropsychologie · 2018
Typearticle
Languagefr
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

Les connaissances liées aux entités uniques, telles que les personnes célèbres, sont particulièrement vulnérables à un déclin précoce dans le trouble cognitif léger (TCL) et la maladie d'Alzheimer (MA).Or, en dépit de son importance clinique, les neuropsychologues québécois disposent de peu d'outils pour évaluer ce type de mémoire.Ce projet présente le POP-40, un protocole d'évaluation de la mémoire sémantique liée aux personnes célèbres, spécifiquement adapté à la population vieillissante du Québec.Son processus d'élaboration y est décrit (étude 1), suivi des résultats d'une collecte de données de référence auprès de 103 participants âgés sains (étude 2).Une version abrégée, le POP-10, a également été développée à partir des résultats d'individus avec TCL pour le dépistage rapide de troubles de mémoire sémantique liée aux personnes célèbres (étude 3).L'utilisation conjointe de ce nouvel outil avec le PUB-40, un protocole d'évaluation de la mémoire des événements publics, est également recommandée. Mots clés : maladie d'

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)0.049
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.093
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.375
Teacher spread0.330 · 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 designBench or experimental
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

Citations1
Published2018
Admission routes2
Has abstractyes

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