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Rehabilitation of discourse impairments after acquired brain injury

2014· article· en· W1543314695 on OpenAlexaff
Gigiane Gindri, Karina Carlesso Pagliarin, Fabíola Schwengber Casarin, Perrine Ferré, Yves Joanette, Róchele Paz Fonseca

Bibliographic record

VenueDementia & Neuropsychologia · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de Montréal
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsRehabilitationAphasiaAcquired brain injuryTraumatic brain injuryPsychologyPhysical medicine and rehabilitationInclusion (mineral)Psychological interventionCognitionMedicinePsychiatryNeuroscience

Abstract

fetched live from OpenAlex

Language impairments in patients with acquired brain injury can have a negative impact on social life as well as on other cognitive domains. Discourse impairments are among the most commonly reported communication deficits among patients with acquired brain damage. Despite advances in the development of diagnostic tools for detecting such impairments, few studies have investigated interventions to rehabilitate patients presenting with these conditions. OBJECTIVE: The aim of this study was to present a systematic review of the methods used in the rehabilitation of discourse following acquired brain injury. METHODS: The PubMed database was searched for articles using the following keywords: "rehabilitation", "neurological injury", "communication" and "discursive abilities". RESULTS: A total of 162 abstracts were found, but only seven of these met criteria for inclusion in the review. Four studies involved samples of individuals with aphasia whereas three studies recruited samples of individuals with traumatic brain injury. CONCLUSION: All but one article found that patient performance improved following participation in a discourse rehabilitation program.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.315
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.365
Teacher spread0.340 · 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 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

Citations15
Published2014
Admission routes1
Has abstractyes

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