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Record W4403257603 · doi:10.1542/peds.2023-065484

Early Childhood Concussion

2024· review· en· W4403257603 on OpenAlexaff
Miriam H. Beauchamp, Vicki Anderson, Linda Ewing‐Cobbs, Juliet Haarbauer‐Krupa, Audrey McKinlay, Shari L. Wade, Stacy J. Suskauer

Bibliographic record

VenuePEDIATRICS · 2024
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsConcussionMedicineEarly childhoodCognitionInjury preventionPoison controlTraumatic brain injuryEpidemiologyPediatricsHuman factors and ergonomicsPhysical therapyPsychiatryDevelopmental psychologyMedical emergencyPsychology

Abstract

fetched live from OpenAlex

The unconsolidated motor and cognitive skills that are typical of the early childhood period place infants, toddlers, and preschoolers at risk for a variety of traumatic injuries. Such injuries may include mild traumatic brain injury or concussion. Knowledge regarding the risk, diagnosis, outcomes, and management of early childhood concussion is limited, especially compared with what is known about concussion in school-age children, adolescents, and adults. This state-of-the-art review aims to provide current knowledge on the epidemiology, physical signs, behavior, and clinical outcomes associated with early childhood concussion. Research on this condition has been challenged by the need to adapt methods to the unique physical, behavioral, and developmental characteristics of young children. We provide information on observable symptoms associated with concussion, recommended approaches to care, and suggestions for overcoming barriers to research in this area. Developmentally appropriate efforts are needed to improve our ability to identify, evaluate, and treat early childhood concussion.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.099
GPT teacher head0.408
Teacher spread0.309 · 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 designNot applicable
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

Citations15
Published2024
Admission routes1
Has abstractyes

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Same venuePEDIATRICSSame topicTraumatic Brain Injury ResearchFrench-language works237,207