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Association Between Early Return to School Following Acute Concussion and Symptom Burden at 2 Weeks Postinjury

2023· article· en· W4317567050 on OpenAlexafffundabout
Christopher G. Vaughan, Andrée‐Anne Ledoux, Maegan Sady, Ken Tang, Keith Owen Yeates, Gurinder Sangha, Martin H. Osmond, Stephen B. Freedman, Jocelyn Gravel, Isabelle Gagnon, William Craig, Emma Burns, Kathy Boutis, Darcy Beer, Gérard A. Gioia, Roger Zemek, Candice McGahern, Angelo Mikrogianakis, Ken J. Farion, Karen Barlow, Alexander Sasha Dubrovsky, Willem Meeuwisse, William P. Meehan, Yael Kamil, Miriam H. Beauchamp, Blaine Hoshizaki, Peter Anderson, Brian L. Brooks, Michael Vassilyadi, Terry P. Klassen, Michelle Keightley, Lawrence Richer, Carol DeMatteo, Nick Barrowman, Mary Aglipay, Anne M. Grool

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsChildren's Hospital of WinnipegSickKids FoundationAlberta Children's HospitalStollery Children's HospitalUniversity of TorontoWestern UniversityMontreal Children's HospitalHospital for Sick ChildrenUniversité de MontréalDalhousie UniversityCentre Hospitalier Universitaire Sainte-JustineChildren's Hospital of Eastern OntarioChildren's Hospital of Western OntarioUniversity of OttawaMcGill UniversityUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineConcussionCohort studyProspective cohort studyCohortEmergency departmentPediatricsPhysical therapyObservational studyInjury preventionPoison controlEmergency medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Importance: Determining how the timing of return to school is related to later symptom burden is important for early postinjury management recommendations. Objective: To examine the typical time to return to school after a concussion and evaluate whether an earlier return to school is associated with symptom burden 14 days postinjury. Design, Setting, and Participants: Planned secondary analysis of a prospective, multicenter observational cohort study from August 2013 to September 2014. Participants aged 5 to 18 years with an acute (<48 hours) concussion were recruited from 9 Canadian pediatric emergency departments in the Pediatric Emergency Research Canada Network. Exposure: The independent variable was the number of days of school missed. Missing fewer than 3 days after concussion was defined as an early return to school. Main Outcomes and Measures: The primary outcome was symptom burden at 14 days, measured with the Post-Concussion Symptom Inventory (PCSI). Symptom burden was defined as symptoms status at 14 days minus preinjury symptoms. Propensity score analyses applying inverse probability of treatment weighting were performed to estimate the relationship between the timing of return to school and symptom burden. Results: This cohort study examined data for 1630 children (mean age [SD] 11.8 [3.4]; 624 [38%] female). Of these children, 875 (53.7%) were classified as having an early return to school. The mean (SD) number of days missed increased across age groups (5-7 years, 2.61 [5.2]; 8-12 years, 3.26 [4.9]; 13-18 years, 4.71 [6.1]). An early return to school was associated with a lower symptom burden 14 days postinjury in the 8 to 12-year and 13 to 18-year age groups, but not in the 5 to 7-year age group. The association between early return and lower symptom burden was stronger in individuals with a higher symptom burden at the time of injury, except those aged 5 to 7 years. Conclusions and Relevance: In this cohort study of youth aged 5 to 18 years, these results supported the growing belief that prolonged absences from school and other life activities after a concussion may be detrimental to recovery. An early return to school may be associated with a lower symptom burden and, ultimately, faster recovery.

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)0.005
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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.356
Teacher spread0.315 · 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

Citations36
Published2023
Admission routes3
Has abstractyes

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