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Record W3003209609 · doi:10.1037/neu0000618

It’s a matter of surgency: Traumatic brain injury is associated with changes in preschoolers’ temperament.

2020· article· en· W3003209609 on OpenAlexafffund
Marilou Séguin, Fanny Dégeilh, Annie Bernier, Ramy El-Jalbout, Miriam H. Beauchamp

Bibliographic record

VenueNeuropsychology · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsTemperamentTraumatic brain injuryPsychologyNegative affectivityClinical psychologyPoison controlInjury preventionDevelopmental psychologyPersonalityPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) can disrupt cognitive, social, and behavioral functioning. Temperament is often used to reflect emotional and behavioral tendencies in young children, but has never been assessed after TBI. OBJECTIVE: Evaluate whether early TBI disrupts the trajectory of temperament. METHOD: = 69) reported on their child's temperament retrospectively to assess preinjury profiles and at 6 and 18 months postinjury. For each domain of temperament (Surgency, Negative Affectivity, Effortful Control), linear mixed-model analyses were conducted to explore group differences on the rate of change across time. RESULTS: = .33. Children with msTBI showed a lower rate of increase in Surgency compared to children with mild TBI and orthopedic injuries. CONCLUSIONS: Developmental trajectories of Surgency appear to be affected by msTBI. Disruptions in expected developmental trajectories of temperament could underlie some of the sociobehavioral manifestations of TBI in this young age group. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.103
GPT teacher head0.362
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2020
Admission routes2
Has abstractyes

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