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Record W4394075322 · doi:10.6084/m9.figshare.19920653

Heterogeneity of repetition abilities in logopenic variant primary progressive aphasia

2022· dataset· en· W4394075322 on OpenAlexaff
Joël Macoir, Liziane Bouvier, Robert Laforce, Laura Monetta

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsPrimary progressive aphasiaRepetition (rhetorical device)AphasiaPsychologyCognitive psychologyNeuroscienceAudiologyMedicineLinguisticsInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT. The differential diagnosis of primary progressive aphasia (PPA) is challenging due to overlapping clinical manifestations of the different variants of the disease. This is particularly true for the logopenic variant of PPA (lvPPA), in which such overlap was reported with regard to impairments in repetition abilities. In this study, four individuals with lvPPA underwent standard neuropsychological and language assessments. The influence of psycholinguistic variables on their performance of in word, nonword and sentence repetition tasks was also specifically explored. Some level of heterogeneity was found in cognitive functions and in language. The four participants showed impairment in sentence repetition in which their performance was negatively affected by semantic reversibility and syntactic complexity. This study supports the heterogeneity of lvPPA with respect to the cognitive and linguistic status of participants. It also shows that sentence repetition is influenced not only by length, but also by semantic reversibility and syntactic complexity, two psycholinguistic variables known to place additional demands on phonological working memory.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.009

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 head0.052
GPT teacher head0.306
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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