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Record W3199795155 · doi:10.3233/prm-200795

Language performance within three months of early childhood traumatic brain injury

2021· article· en· W3199795155 on OpenAlexaff
Carly A. Cermak, Shannon E. Scratch, Nick Reed, Deryk S. Beal

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsTraumatic brain injuryReceptive languageExpressive languagePsychologyRehabilitationLanguage developmentMedicineDevelopmental psychologyPhysical therapyPsychiatryLinguisticsVocabulary

Abstract

fetched live from OpenAlex

PURPOSE: To examine language outcomes in the short-term stage (i.e., within three months) of early childhood traumatic brain injury (TBI). METHODS: A retrospective chart review over a 10-year period (January 1, 2007 to December 31, 2016) was completed at a single-site inpatient rehabilitation hospital. Inclusion criteria were children aged 15 months to five years 11 months with a diagnosis of closed TBI. RESULTS: Twenty-four charts were included in the descriptive analysis of language; there were fewer children with expressive language scores (n = 18) than receptive language scores (n = 24), likely due to word retrieval difficulties as per clinical documentation. Effects of TBI on language performance were more pronounced in receptive than expressive language. For children with scores in both receptive and expressive language areas (n = 18), five children had below average scores. These children were described as having language delays pre-injury (n = 2), lower exposure to English (n = 1), information processing difficulties (n = 1), and difficulties with formulation and organization of language (n = 1). CONCLUSION: This study represents an initial step in understanding expressive and receptive language performance shortly after early childhood TBI. Challenges with assessment as well as directions for future research are discussed.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.335
Teacher spread0.307 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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