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Record W2193995579 · doi:10.1371/journal.pone.0135860

Energy Drinks, Alcohol, Sports and Traumatic Brain Injuries among Adolescents

2015· article· en· W2193995579 on OpenAlexafffundabout
G. Ilie, Angela Boak, Robert E. Mann, Edward M. Adlaf, Hayley A. Hamilton, Mark Asbridge, Jürgen Rehm, Michael D. Cusimano

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsDalhousie UniversityCentre for Addiction and Mental HealthSt. Michael's HospitalPublic Health OntarioUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchIndustry CanadaOntario Ministry of Health and Long-Term CareOntario Neurotrauma FoundationCentre for Addiction and Mental Health
KeywordsMedicinePoison controlInjury preventionPopulationOccupational safety and healthAthletesOdds ratioOddsCross-sectional studyTraumatic brain injuryYoung adultSuicide preventionLogistic regressionEnvironmental healthPsychiatryPhysical therapyGerontologyInternal medicine

Abstract

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IMPORTANCE: The high prevalence of traumatic brain injuries (TBI) among adolescents has brought much focus to this area in recent years. Sports injuries have been identified as a main mechanism. Although energy drinks, including those mixed with alcohol, are often used by young athletes and other adolescents they have not been examined in relation to TBI. OBJECTIVE: We report on the prevalence of adolescent TBI and its associations with energy drinks, alcohol and energy drink mixed in with alcohol consumption. DESIGN, SETTINGS AND PARTICIPANTS: Data were derived from the Centre for Addiction and Mental Health's 2013 Ontario Student Drug Use and Health Survey (OSDUHS). This population-based cross-sectional school survey included 10,272 7th to 12th graders (ages 11-20) who completed anonymous self-administered questionnaires in classrooms. MAIN OUTCOME MEASURES: Mild to severe TBI were defined as those resulting in a loss of consciousness for at least five minutes, or being hospitalized for at least one night. Mechanism of TBI, prevalence estimates of TBI, and odds of energy drink consumption, alcohol use, and consumption of energy drinks mixed with alcohol are assessed. RESULTS: Among all students, 22.4% (95% CI: 20.7, 24.1) reported a history of TBI. Sports injuries remain the main mechanism of a recent (past year) TBI (45.5%, 95% CI: 41.0, 50.1). Multinomial logistic regression showed that relative to adolescents who never sustained a TBI, the odds of sustaining a recent TBI were greater for those consuming alcohol, energy drinks, and energy drinks mixed in with alcohol than abstainers. Odds ratios were higher for these behaviors among students who sustained a recent TBI than those who sustained a former TBI (lifetime but not past 12 months). Relative to recent TBI due to other causes of injury, adolescents who sustained a recent TBI while playing sports had higher odds of recent energy drinks consumption than abstainers. CONCLUSIONS AND RELEVANCE: TBI remains a disabling and common condition among adolescents and the consumption of alcohol, energy drinks, and alcohol mixed with energy drinks further increase the odds of TBI among adolescents. These associations warrant further investigation.

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)0.002
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.136
GPT teacher head0.315
Teacher spread0.179 · 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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Citations26
Published2015
Admission routes3
Has abstractyes

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