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Record W2137340721 · doi:10.1684/pnv.2012.0368

Non-pharmacological therapies of language deficits in semantic dementia

2012· review· fr· W2137340721 on OpenAlexaff
Karine Gravel-Laflamme, Sonia Routhier, Joël Macoir

Bibliographic record

VenueGériatrie et Psychologie Neuropsychiatrie du Viellissement · 2012
Typereview
Languagefr
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDementiaPsychological interventionSemantic dementiaPsychologyComprehensionAugmentative and alternative communicationSemantic memoryCognitionSemantics (computer science)Cognitive psychologyDiseaseMedicineComputer sciencePsychiatryFrontotemporal dementiaPathology

Abstract

fetched live from OpenAlex

Semantic dementia (SD) is a neurodegenerative condition characterised by a progressive disorder of semantic processing, word comprehension and anomia. This literature review reports behavioural studies about language therapies for SD. More precisely, the review presents the cognitive, participative and alternative/augmentative interventions reported in the literature to improve language performances or to compensate for language worsening associated with the disease. Most studies show that interventions are efficient. However, maintenance of improvement and generalization to untreated language abilities remain limited. Other studies are still required to establish the clinical relevance of interventions for language and communication disorders in semantic dementia. In these studies, the use of more ecological interventions focusing on the specific needs of people living with semantic dementia should be specifically addressed.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.060
GPT teacher head0.366
Teacher spread0.307 · 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
GenreReview

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

Citations6
Published2012
Admission routes1
Has abstractyes

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