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Record W3209450527 · doi:10.1044/2021_ajslp-20-00299

Discourse Performance in Adults With Mild Traumatic Brain Injury, Orthopedic Injuries, and Moderate to Severe Traumatic Brain Injury, and Healthy Controls

2022· article· en· W3209450527 on OpenAlexaff

Bibliographic record

VenueAmerican Journal of Speech-Language Pathology · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcMaster University
FundersNational Center for Advancing Translational SciencesNational Institute of General Medical Sciences
KeywordsTraumatic brain injuryCognitionPoison controlHuman factors and ergonomicsInjury preventionOrthopedic surgeryOccupational safety and health

Abstract

fetched live from OpenAlex

INTRODUCTION: Adults with mild traumatic brain injury (mTBI) are at risk for communication disorders, yet studies exploring cognitive-communication performance are currently lacking. AIMS: This aim of this study was to characterize discourse-level performance by adults with mTBI on a standardized elicitation task and compare it to (a) healthy adults, (b) adults with orthopedic injuries (OIs), and (c) adults with moderate to severe TBI. METHOD: This study used a cross-sectional design. The participants included mTBI and OI groups recruited prospectively from an emergency medicine department. Moderate to severe TBI and healthy data were acquired from TalkBank. One-way analyses of variance were used to compare mean linguistic scores. RESULTS: Seventy participants across all groups were recruited. Groups did not differ on demographic variables. The study found significant differences in both content and productivity measures among the groups. Variables did not appear sensitive to differentiate between mTBI and OI groups. DISCUSSION: Cognitive and language performance of adults with mTBI is a pressing clinical issue. Studies exploring language with carefully selected control groups can influence the development of sensitive measures to identify individuals with cognitive-communication deficits.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.341
Teacher spread0.317 · 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.

Study designOther design
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

Citations17
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Speech-Language PathologySame topicTraumatic Brain Injury ResearchFrench-language works237,207