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Record W4307758928 · doi:10.1080/02699052.2022.2140197

The negative influence of chronic alcohol abuse on acute cognitive recovery after a traumatic brain injury

2022· article· en· W4307758928 on OpenAlexaff
Sarah-Jade Roy, Camille Livernoche Leduc, Véronique Paradis, Gabrielle Cataford, Marie-Julie Potvin

Bibliographic record

VenueBrain Injury · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital du Sacré-Cœur de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsTraumatic brain injuryMedicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Cognitive recovery after a traumatic brain injury (TBI) may be negatively affected by a prior alcohol use disorder (AUD). This study aims to compare the cognitive recovery of patients who had comorbid TBI and AUD relative to TBI alone and investigate the influence of blood alcohol level (BAL) at hospital admission on this recovery. METHOD: The sample consisted of 42 patients who had sustained a TBI (mild or moderate) and had an AUD diagnosis (TBI+AUD), and 42 patients who had sustained a TBI alone (TBI). The Brief Cognitive Exam in Traumatology (EXACT), designed to evaluate cognitive functions in the acute phase of TBI was administered (± 2 weeks post-injury). RESULTS: After controlling for BAL at admission, the TBI+AUD group had a lower EXACT total score compared to the TBI group. The negative influence of age on the results was more pronounced in the TBI+AUD group. The number of intoxicated patients at admission was also higher in this group, although there was no correlation between BAL at admission and cognitive outcome. CONCLUSION: The presence of an AUD diagnosis seems to exert a greater negative influence on cognitive recovery following a mild/moderate TBI than BAL at admission, especially in older patients.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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