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Record W4318263573 · doi:10.1111/1460-6984.12844

The longitudinal trajectory of discourse from the hyperacute to the chronic phase in mild to moderate poststroke aphasia recovery: A case series study

2023· article· en· W4318263573 on OpenAlexafffund
Amélie Brisebois, Simona M. Brambati, Elizabeth Rochon, Carol Léonard, Karine Marcotte

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of OttawaToronto Rehabilitation InstituteHeart and Stroke FoundationUniversity of TorontoUniversity Health NetworkInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersHeart and Stroke Foundation of Canada
KeywordsAphasiaPsychologyNarrativeLongitudinal studyDiscourse analysisStroke (engine)Cognitive psychologyLanguage disorderRehabilitationDevelopmental psychologyLinguisticsAudiologyCognitionMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Discourse analysis has recently received much attention in the aphasia literature. Even if post-stroke language recovery occurs throughout the longitudinal continuum of recovery, very few studies have documented discourse changes from the hyperacute to the chronic phases of recovery. AIMS: To document a multilevel analysis of discourse changes from the hyperacute phase to the chronic phase of post-stroke recovery using a series of single cases study designs. METHODS & PROCEDURES: Four people with mild to moderate post-stroke aphasia underwent four assessments (hyperacute: 0-24 h; acute: 24-72 h; subacute: 7-14 days; and chronic: 6-12 months post-onset). Three discourse tasks were performed at each time point: a picture description, a personal narrative and a story retelling. Multilevel changes in terms of macro- and microstructural aspects were analysed. The results of each discourse task were combined for each time point. Individual effect sizes were computed to evaluate the relative strength of changes in an early and a late recovery time frame. OUTCOMES & RESULTS: Macrostructural results revealed improvements throughout the recovery continuum in terms of coherence and thematic efficiency. Also, the microstructural results demonstrated linguistic output improvement for three out of four participants. Namely, lexical diversity and the number of correct information units/min showed a greater gain in the early compared with the late recovery phase. CONCLUSIONS & IMPLICATIONS: This study highlights the importance of investigating all discourse processing levels as the longitudinal changes in discourse operate differently at each phase of recovery. Overall results support future longitudinal discourse investigation in people with post-stroke aphasia. WHAT THIS PAPER ADDS: What is already known on the subject Multi-level discourse analysis allows for in-depth analysis of underlying discourse processes. To date, very little is known on the longitudinal discourse changes from aphasia onset through to the chronic stage of recovery. This study documents multi-level discourse features in four people with mild to moderate aphasia in the hyperacute, acute, subacute and chronic stage of post-stroke aphasia recovery. What this paper adds to existing knowledge The study found that most discourse variables demonstrated improvement throughout time. Macrostructural variables of coherence and thematic units improved throughout the continuum whereas microstructural variables demonstrated greater gains in the early compared to the late period of recovery. What are the potential or actual clinical implications of this work? This study suggests that multilevel discourse analysis will allow a better understanding of post-stroke aphasia recovery, although more research is needed to determine the clinical utility of these findings. Future research may wish to investigate longitudinal discourse recovery in a larger sample of people with aphasia with heterogenous aphasia profiles and severities.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.877

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 science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.378
Teacher spread0.342 · 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 designQualitative
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

Citations7
Published2023
Admission routes2
Has abstractyes

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