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Record W1988823313 · doi:10.1159/000340017

Communicative Clusters after a Right-Hemisphere Stroke: Are There Universal Clinical Profiles?

2012· article· en· W1988823313 on OpenAlexaff
Perrine Ferré, Róchele Paz Fonseca, Bernadette Ska, Yves Joanette

Bibliographic record

VenueFolia Phoniatrica et Logopaedica · 2012
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoMinisterio de Economía y Competitividad
KeywordsRight hemispherePsychologyNeuropsychologyLateralization of brain functionStroke (engine)Developmental psychologyNeuroscienceCognitive psychologyCognition

Abstract

fetched live from OpenAlex

OBJECTIVE: The current research aimed at classifying communication profiles among right-brain-damaged adults with an intercultural perspective, and so begins to fill in a long-standing gap in the literature. METHOD: The sample was made up of 112 right-brain-damaged individuals from three nationalities (Canadians, Brazilians and Argentineans). They were assessed using 13 language tasks from the Protocol MEC in Spanish, Brazilian Portuguese and French. RESULTS: A hierarchical cluster analysis led to four distinct clinical profiles of communication. Since only a few distinctions between nationalities were observed, the results suggest that there probably is a partial universality of clinical profiles of communication impairments after a right brain damage. CONCLUSIONS: This study proposes a preliminary taxonomy of communication disorders among right-brain-damaged individuals with cross-cultural implications. The exploration of associated stroke sites and neuropsychological concomitant deficits would contribute to the eventual development of a more accurate clinical intervention.

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)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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

Citations37
Published2012
Admission routes1
Has abstractyes

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