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Record W2754819360 · doi:10.1097/htr.0000000000000341

Alcohol and Drug Use Before and During the First Year After Traumatic Brain Injury

2017· article· en· W2754819360 on OpenAlexaff
Simon Beaulieu‐Bonneau, Frédéric St‐Onge, Marie-Claude Blackburn, Andrée Banville, Andrée-Anne Paradis-Giroux, Marie‐Christine Ouellet

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTraumatic brain injuryDrugMedicinePsychologyPsychiatryAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare individuals with mild and moderate/severe traumatic brain injury (TBI) on alcohol and drug use and substance use disorders before and in the first year post-TBI; to explore sociodemographic and injury-related variables associated with substance use disorders. PARTICIPANTS: A total of 225 adults hospitalized in a level I trauma center after TBI. DESIGN: Observational cohort study with retrospective (pre-TBI) and prospective (4, 8, and 12 months post-TBI) assessments. MAIN MEASURES: Mini International Neuropsychiatric Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). RESULTS: The percentage of participants using alcohol or drug declined shortly after the injury (4 months) but increased closer to preinjury levels by the end of the first year. Post-TBI alcohol use was higher after mild than moderate/severe TBI, but drug use was similar. About 11% of participants met criteria for a substance use disorder in the first year after TBI. Younger age, not being in a relationship, and suspected substance intoxication at the time of TBI were associated with the presence of a post-TBI substance use disorder. CONCLUSION: Individuals with milder injuries return to alcohol use earlier than those with more severe injuries. Given that substance use may alter recovery, preventive recommendations and systematic follow-ups are warranted regardless of injury severity and access to rehabilitation.

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)0.002
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 score0.061
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.354
Teacher spread0.316 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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