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Record W3010817352 · doi:10.1080/17518423.2020.1736683

Health-Related Quality of Life in Non-Concussed Children: A Normative Study to Inform Concussion Management

2020· article· en· W3010817352 on OpenAlexaff
Melissa Paniccia, Christina Ippolito, Stephanie McFarland, James Murphy, Nick Reed

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsConcussionHeadachesNormativeQuality of life (healthcare)MedicinePhysical therapyHealth related quality of lifeInjury preventionPoison controlClinical psychologyPsychologyPsychiatryMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

Purpose: There has been a shift to consider pediatric concussion recovery beyond symptom management by considering how health-related quality of life (HRQoL) affects recovery. This study investigated normative ranges of HRQoL in children and explored its relationship with common pediatric concussion variables.Methods: A cross-sectional study of 1,722 non-concussed children 8–12 years old (M = 10.52 ± 1.23 years; 1,335 males, 387 females) was conducted by secondary analysis of clinical baseline concussion data. Demographic information, concussion-like symptoms (PCSI-C), and HRQoL (KIDSCREEN-10 Index) were self-reported.Results: The most reported concussion-like symptoms were common stress symptoms and were significantly negatively correlated with HRQoL. Premorbid histories of attention deficit hyperactivity disorder, mental health challenges, headaches/migraines, and concussion significantly lowered HRQoL. The number of diagnosed concussions and PCSI-C scores were significantly negatively correlated with HRQoL.Conclusions: The normative ranges and model can indicate HRQoL levels to inform clinicians how children may respond to concussion and streamline care beyond traditional assessment models.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.150
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.361
Teacher spread0.298 · 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.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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