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Record W2972283748 · doi:10.1097/htr.0000000000000515

Pragmatic Language Comprehension After Pediatric Traumatic Brain Injury: A Scoping Review

2019· review· en· W2972283748 on OpenAlexaff
Stephanie Deighton, Narae Ju, Susan A. Graham, Keith Owen Yeates

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2019
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAlberta Children’s Hospital FoundationAlberta Children's Hospital
Fundersnot available
KeywordsComprehensionPsycINFOPsychologyLiteral and figurative languagePraiseTraumatic brain injuryMEDLINEDevelopmental psychologyLinguisticsPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This scoping review aims to examine the literature pertaining to pragmatic language comprehension in pediatric traumatic brain injury (TBI), in order to summarize the current evidence and to identify areas for further research. METHODS: We searched MEDLINE Ovid and PsycINFO Ovid using search terms to identify all articles that examined pragmatic language comprehension in children and adolescents with TBI published until November 2017. RESULTS: A total of 13 articles met our inclusion criteria. The studies included examined a number of pragmatic domains including knowledge-based and pragmatic inferences, detection and judgment of ambiguous sentences, comprehension of humor, understanding of figurative language (eg, metaphors and idioms), and comprehension of irony and deceptive praise. CONCLUSION: The research suggests that children and adolescents with TBI, as compared with healthy or orthopedically injured controls, display deficits in comprehension of pragmatic language. Children with severe TBI demonstrate more widespread deficits in pragmatic comprehension abilities, whereas children with mild TBI show relatively intact pragmatic comprehension. Limitations and gaps identified in the literature warrant further research in this area.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.109
GPT teacher head0.472
Teacher spread0.363 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2019
Admission routes1
Has abstractyes

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