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Association of Persistent Postconcussion Symptoms With Pediatric Quality of Life

2016· article· en· W2538850034 on OpenAlexaffabout
Zuzana Novak, Mary Aglipay, Nick Barrowman, Keith Owen Yeates, Miriam H. Beauchamp, Jocelyn Gravel, Stephen B. Freedman, Isabelle Gagnon, Gérard A. Gioia, Kathy Boutis, Emma Burns, Andrée‐Anne Ledoux, Martin H. Osmond, Roger Zemek

Bibliographic record

VenueJAMA Pediatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHospital for Sick ChildrenMcGill University Health CentreIzaak Walton Killam Health CentreMontreal Children's HospitalCentre Hospitalier Universitaire Sainte-JustineAlberta Children's HospitalUniversité de MontréalUniversity of CalgaryChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineInterquartile rangeQuality of life (healthcare)ConcussionEmergency departmentHead injuryPediatricsAnxietyProspective cohort studyDepression (economics)CohortPhysical therapyPoison controlInjury preventionInternal medicineEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

IMPORTANCE: Persistent postconcussion symptoms (PPCS) pose long-term challenges and can negatively affect patients' health-related quality of life (HRQoL). To date, no large comprehensive study has addressed the association between PPCS and HRQoL. OBJECTIVES: To determine the association between HRQoL and PPCS at 4 weeks after concussion and assess the degree of impairment of HRQoL in the subsequent 12 weeks. DESIGN, SETTING, AND PARTICIPANTS: In a prospective, multicenter cohort study (Predicting Persistent Postconcussive Problems in Pediatrics [5P]) from August 14, 2013, to September 30, 2014, children aged 5 to 18 years who presented to the emergency department within 48 hours after head injury and were considered to have an acute concussion were enrolled across 9 pediatric emergency departments within the Pediatric Emergency Research Canada Network. Persistent postconcussion symptoms were defined as 3 or more persistent symptoms on the validated Post-Concussion Symptom Inventory at 4 weeks. Linear mixed effects random coefficients models evaluated the association between PPCS and HRQoL, adjusting for potential confounders including age, sex, prior concussions, migraine, anxiety, learning disability, depression, and sleep disorder. MAIN OUTCOMES AND MEASURES: The primary outcome was HRQoL assessed with the validated Pediatric Quality of Life Inventory version 4.0 (PedsQL-4.0) at 4, 8, and 12 weeks after head injury. RESULTS: Of 2006 children enrolled (median age, 11.8 years [interquartile range, 8.9-14.6 years]; 1241 boys and 765 girls), 1667 (83.1%) completed the PedsQL-4.0 at all 3 time points. Of these 1667 children, the 510 with PPCS (30.6%) had lower total PedsQL-4.0 scores (mean, 70.0) than did those without PPCS (mean, 80.3; mean difference, -10.3; 95% CI, -9.4 to -11.2). Patients with PPCS also had significantly lower physical, emotional, social, and school PedsQL-4.0 subscores at 4, 8, and 12 weeks. Patients with PPCS had lower HRQoL than published healthy norms at 4 weeks (mean difference, 13.89; 95% CI, 11.55-16.23), 8 weeks (mean difference, 11.63; 95% CI, 9.34-13.93), and 12 weeks (mean difference, 9.38; 95% CI, 7.01-11.75; P < .001). Patients who recovered from concussion also had lower HRQoL than norms at 4 weeks (mean difference, 3.56; 95% CI, 1.28-5.85) and 8 weeks (mean difference, 2.75; 95% CI, 0.48-5.02; P < .05). School functioning PedsQL-4.0 subscores were significantly lower for all children regardless of PPCS status at all time points. CONCLUSIONS AND RELEVANCE: Children with PPCS have lower HRQoL compared with those who have recovered from concussion, yet deficits in HRQoL are pervasive across all domains and may persist for months even in children whose symptoms have resolved. Future interventional research should target the effect of concussion on HRQoL.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.314
Teacher spread0.273 · 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".

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Citations208
Published2016
Admission routes2
Has abstractyes

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