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Record W2035597122 · doi:10.3109/02699052.2010.489794

Effect of blood alcohol level on Glasgow Coma Scale scores following traumatic brain injury

2010· article· en· W2035597122 on OpenAlexaff
Rael T. Lange, Grant L. Iverson, Jeffrey R. Brubacher, Michael D. Franzen

Bibliographic record

VenueBrain Injury · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaBC Mental Health & Substance Use Services
Fundersnot available
KeywordsGlasgow Coma ScaleTraumatic brain injuryMedicineBlood alcoholPoison controlComa (optics)Injury preventionPsychologyEmergency medicineAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: It is a common clinical perception that alcohol intoxication systematically lowers Glasgow Coma Scale (GCS) scores when evaluating traumatic brain injury (TBI). However, the research findings in this area do not uniformly support this notion. The purpose of this study is to examine the effects of blood alcohol level (BAL) on GCS scores following TBI. METHOD: Participants were 475 patients (64% male) who presented to a Level 1 trauma centre following a TBI. Patients were selected if they were injured in a motor vehicle accident and had an available day-of-injury GCS, BAL and Computed Tomography (CT) brain scan. RESULTS: Overall, acute alcohol intoxication did not significantly affect GCS scores, even in patients with BALs of 200 mg dl(-1) or higher. When controlling for the effects of injury severity, acute alcohol intoxication affected GCS scores only in those patients with BALs greater than 200 mg dl(-1) who also had intracranial abnormalities detected on CT scan. CONCLUSIONS: These findings suggest that GCS scores can be interpreted at face value in the vast majority of patients who are intoxicated. However, GCS scores will likely over-estimate the severity of brain injury in patients with abnormal head CT scans and BALs greater than 200 mg dl(-1).

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.314
Teacher spread0.286 · 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 designBench or experimental
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

Citations29
Published2010
Admission routes1
Has abstractyes

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