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Record W4406679005 · doi:10.1080/02699052.2025.2451193

Improving the early detection of aphasia in the acute phase of stroke: the contribution of a screening test

2025· article· en· W4406679005 on OpenAlexaff
Marie-Hélène Lavoie, Anne-Claire Albiseti, Stéphanie Gosselin-Lefebvre, Joël Macoir

Bibliographic record

VenueBrain Injury · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsAphasiaStroke (engine)Acute strokeTest (biology)Physical medicine and rehabilitationScreening testPsychologyMedicineAudiologyPediatricsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Aphasia is one of the most common and most debilitating after-effects of a stroke. In the acute phase of a stroke, referrals to speech-language pathology (SLP) are frequently guided by clinical impressions rather than validated tests. OBJECTIVES: This study aimed to evaluate the advantages of incorporating the Screening test for language disorders in adults and the elderly (DTLA) into clinical practice for detecting language disorders during the acute phase of stroke. METHODS: The study includes a retrospective and a prospective component, including a questionnaire on the acceptability, feasibility and usefulness of using the DTLA in patients in the acute phase of stroke. RESULTS: Sixty-one patients admitted for stroke were recruited for each of the two components. The introduction of the DTLA in the prospective component of the study had a significant impact on the detection of language impairment, as more notes about language were found in patients' medical records and more referrals were made to SLP. CONCLUSIONS: Using a screening test can improve the detection of aphasia during the acute phase of stroke, particularly in patients whose impairments might not be easily identified through subjective assessments.

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)0.003
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 score0.015
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0000.000

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 head0.016
GPT teacher head0.312
Teacher spread0.296 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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