Differential Effects of Alcohol Intoxication on S100B Levels Following Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: In an acute care setting, evaluation of traumatic brain injury (TBI) is often complicated by alcohol intoxication. The purpose of this study is to evaluate the clinical utility of the protein S100B as a biochemical marker for identifying brain injury in patients who are intoxicated at the time of injury. METHODS: The study participants were 160 patients who presented to a large urban Level I Trauma Centre in Vancouver, Canada. Patients were classified into four clinical groups (medical controls, trauma controls, mild TBI, and definite TBI) and two day-of-injury alcohol intoxication groups (i.e., sober and intoxicated). Blood samples were collected via venipuncture in heparinized tubes within 8 hours of injury. Measures of S100B concentration were obtained using a commercially available assay kit (Sangtec 100 Elisa). RESULTS: For those patients who were sober at the time of injury, higher S100B levels were associated with TBI when compared with other physical injuries and general medical complaints. However, for patients who were intoxicated at the time of injury, there were uniformly low S100B levels across all clinical groups. CONCLUSIONS: Although there seems to be a strong association between S100B levels and TBI, further research is required to establish the clinical role of S100B in patients with suspected TBI, particularly in patients whose clinical presentation is complicated by alcohol intoxication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".