Can Admission S-100β Predict the Extent of Brain Damage in Head Trauma Patients?
Bibliographic record
Abstract
BACKGROUND: As has been shown previously, S-100beta levels in serum can be a useful predictor of brain damage after head trauma. This pilot study was designed to investigate whether urine samples, which are much easier to obtain, could be used for the same purpose instead of serum samples. METHODS: Ninety-six consecutive patients admitted with head trauma were recruited in the study. After exclusion of 54 patients, mostly because of significant additional trauma, S-100beta levels were analyzed in serum and urine of 42 patients using a luminometric assay. A range for normal values was established based on samples from ten healthy volunteers. RESULTS: S-100beta serum levels increased proportional to the severity of the head trauma, as had been previously shown by several other groups. In many patients, initial increases in urine S-100beta levels were seen later than in serum, after which the kinetics of S-100beta levels in urine seemed to follow that established for serum levels. S-100beta values in urine were on average about 54% lower in urine than in serum. CONCLUSIONS: S-100beta levels in urine obtained on admission to the hospital are not a good indicator for the extent of brain damage. However, urine S-100beta levels obtained at later time points might be a useful indicator for the development of secondary brain injury.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".