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Record W2606721888 · doi:10.1097/htr.0000000000000298

Do Concussive Symptoms Really Resolve in Young Children?

2017· article· en· W2606721888 on OpenAlexaff
Coco Bernard, Jennie Ponsford, Audrey McKinlay, Dean McKenzie, David Krieser

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsKruger (Canada)
Fundersnot available
KeywordsConcussionPsychologyMedicinePoison controlInjury preventionMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the frequency and nature of postconcussive symptoms (PCSs) and behavioral outcomes in young children following mild traumatic brain injury (mTBI) or concussion. SETTING: Emergency department. PARTICIPANTS: Children aged 2 to 12 years presenting with either a concussion or minor bodily injury (control). OUTCOME MEASUREMENT: Parent ratings of PCS were obtained within 72 hours of injury, at 1 week, and 1, 2, and 3 months postinjury using a comprehensive PCS checklist. Preinjury behavior was examined at baseline using the Clinical Assessment of Behavior, which was readministered 1 and 3 months postinjury. RESULTS: PCS burden following mTBI peaked in the acute phase postinjury but reduced significantly from 1 week to 1 month postinjury. Parents of children with mTBI reported more persistent PCSs up to 3 months postinjury than trauma controls, characterized mostly by behavioral and sleep-related symptoms. Subtle increases in problematic behaviors were observed from baseline (preinjury) to 1 month postinjury and persisted at 3 months postinjury; however, scores were not classified as clinically "at risk." CONCLUSIONS: A significant minority of young children experienced persistent PCS and problematic behavior following mTBI. Care must be taken when assessing PCS in younger children as method of PCS assessment may influence parental reporting.

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)0.006
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.377
Teacher spread0.344 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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