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Record W2466794995 · doi:10.1044/persp1.sig2.106

Communication Abilities Following Right Hemisphere Damage: Prevalence, Evaluation, and Profiles

2016· article· en· W2466794995 on OpenAlexaff
Perrine Ferré, Yves Joanette

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsRight hemispherePsychologyLateralization of brain functionAffect (linguistics)Traumatic brain injuryDiseaseBrain functionPopulationStroke (engine)MedicineNeuroscienceCognitive psychologyPsychiatryPathologyCommunication

Abstract

fetched live from OpenAlex

It is now consensually accepted that the contribution of both hemispheres is required to reach a functional level of communication. The unilateralized view of language function, introduced more than a century ago, has since been complemented by clinical experience as well as neuro-imaging observations. Studies based on healthy and right-brain-damaged individuals assert the necessity to better describe, assess, and care for this broad population. Indeed, various neurological conditions, including stroke, traumatic brain injury (TBI), or neurodegenerative disease, can affect the right hemisphere (RH) and lead to distinct communication disorders. In the past 30 years, knowledge about communication assessment and, more recently, therapy designed for right-brain-damaged adults has drastically evolved. This manuscript aims at presenting the theoretical and clinical background that established the current expertise to support accurate assessment of communication following right brain damage. It is believed that a better understanding of the various profiles of impairments following a RH infract will allow speech-language pathologists (SLPs) to develop the clinical awareness necessary for appropriately taking care of these individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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 teacher head, 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

Citations15
Published2016
Admission routes1
Has abstractyes

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