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Record W2756238214 · doi:10.1097/htr.0000000000000343

Depression and Depressive Symptoms in Pediatric Traumatic Brain Injury: A Scoping Review

2017· review· en· W2756238214 on OpenAlexaff
Christianne Laliberté Durish, Rosemary S. Pereverseff, Keith Owen Yeates

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2017
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsycINFOTraumatic brain injuryDepression (economics)MedicineDepressive symptomsPopulationPsychiatryPoison controlInjury preventionClinical psychologyMEDLINEAnxietyMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: This scoping review aimed to summarize the existing knowledge base regarding depression and depressive symptoms in pediatric traumatic brain injury (TBI) and to identify gaps in the literature in an effort to guide future research. METHODS: MEDLINE Ovid and PsycINFO Ovid databases were each searched by the authors using search terms intended to identify any original research study that examined depressive symptoms in children (ie, aged 0-18 years) with TBI. RESULTS: A total of 14 published studies were included in the review. The studies included examined the prevalence of depression, risk factors associated with depression, and depression as a predictor of other TBI-related outcomes. CONCLUSION: Existing research suggests that depressive symptoms are more common in a TBI population than in a healthy or orthopedically injured population. Injury-related factors such as lesions in the brain and the presence of pain, as well as noninjury factors such as older age at injury and low socioeconomic status, may be predictive of depressive symptoms. Depression is likely a secondary outcome of pediatric TBI rather than a direct result of the injury itself. Overall, a relative dearth of research exists on this topic; thus, the review concludes by proposing future research directions.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.143
GPT teacher head0.488
Teacher spread0.345 · 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

Citations72
Published2017
Admission routes1
Has abstractyes

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