MétaCan
Menu
Back to cohort
Record W4210878887 · doi:10.1097/htr.0000000000000766

Parent Interventions Improve Behavior After Pediatric Traumatic Brain Injury: A Systematic Review and Meta-analysis

2022· review· en· W4210878887 on OpenAlexaff
Carly A. Cermak, Sarah McCabe, Brianna Kuchurean, Julia Schaefer, Anna Tendera, Deryk S. Beal

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2022
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsMeta-analysisTraumatic brain injuryPsychological interventionMedicinePsychologyPhysical medicine and rehabilitationPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine child behavior change scores from randomized controlled trials (RCTs) of parent interventions for pediatric traumatic brain injury (TBI). METHODS: MEDLINE, EMBASE, PsycINFO, and CINAHL were searched to identify studies that examined parent interventions for pediatric TBI. Inclusion criteria included (i) a parent intervention for children with TBI; (ii) an RCT study design; (iii) statistical data for child behavior outcome(s); and (iv) studies that were published in English. RESULTS: Seven studies met inclusion criteria. All interventions reported improved child behavior after pediatric TBI; however, child and parent factors contributed to behavior change scores in some interventions. Factors found to contribute to the level of benefit included age of child, baseline behavior levels, sociodemographics (eg, parent income, parent education), and parent mental health. CONCLUSION: Improved child behavior outcomes resulting from parent interventions for pediatric TBI are well supported by the evidence in the peer-reviewed literature. Clinicians are encouraged to consider child and parent factors as they relate to child behavior outcomes.

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.244
GPT teacher head0.485
Teacher spread0.241 · 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 designMeta-analysis
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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Head Trauma RehabilitationSame topicTraumatic Brain Injury ResearchFrench-language works237,207