Clinical Utility of the Protein S100B to Evaluate Traumatic Brain Injury in the Presence of Acute Alcohol Intoxication
Bibliographic record
Abstract
OBJECTIVE: To examine the role of the protein S100B as a biomarker for traumatic brain injury (TBI) in the presence of acute alcohol intoxication. PARTICIPANTS: A total of 159 patients who presented to a large urban level 1 trauma center in Vancouver, British Columbia, Canada, were included. Patients were classified into 4 clinical groups-medical controls (MC), trauma controls (TC), uncomplicated mild TBI (MTBI), and definite TBI (DTBI)-and 2 day-of-injury alcohol intoxication groups (ie, sober and intoxicated). PROCEDURE: Blood samples were collected within 8 hours of injury. MAIN OUTCOME MEASURE: Protein S100B concentration (μg/L; Sangtec 100 Elisa, DiaSorin, Stillwater, Minnesota). RESULTS: Higher S100B levels were found in patients who sustained a TBI than in those in the MC and TC groups (DTBI & MTBI >TC & MC). There was a positive linear relation between S100B levels and brain injury severity (DTBI > MTBI). Alcohol consumption at the time of injury did not generally affect S100B levels. The S100B levels had medium diagnostic accuracy for the majority of patients, with the exception of the DTBI-sober group in which S100B levels had very high diagnostic accuracy. CONCLUSION: Patients with uncomplicated MTBIs and DTBIs had much higher levels of S100B than MC and TC participants. This biomarker had medium diagnostic accuracy for detecting DTBI in the presence of alcohol intoxication and very high accuracy in sober 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".