The Value of Serum Biomarkers in Prediction Models of Outcome After Mild Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: To determine, using a civilian model of mild traumatic brain injury (TBI), the added value of biomarker sampling upon prognostication of outcome at 1 week and 6 weeks postinjury. METHODS: The Galveston Orientation and Amnesia test was administered, and blood samples for serum protein S100B and neuron-specific enolase (NSE) were collected from 141 emergency department patients within 4 hours of a suspected mild TBI (mTBI). The Rivermead Post-Concussion Symptoms Questionnaire (RPQ) was administered via telephone 3 days postinjury. Patients were assessed by a physician at 1 week (n = 113; 80%) and 6 weeks (n = 95; 67%) postinjury. Neurocognitive and postural stability measures were also administered at these follow-ups. RESULTS: Levels of S100B and NSE were found to be abnormally elevated in 49% and 65% of patients with TBI, respectively. Sixty-eight percent and 38% of the patients were considered impaired at 1 week and 6 weeks postinjury, respectively. Stepwise logistic regression modeling identified admission Galveston Orientation and Amnesia test score, S100B level, and RPQ score at day 3 postinjury to be predictive of poor outcome at 1 week postinjury (c-statistic 0.877); female gender, loss of consciousness, NSE level, and RPQ score at day 3 postinjury were predictive of poor outcome at 6 weeks postinjury (c-statistic 0.895). The discriminative power of the biomarkers alone was limited. CONCLUSIONS: Biomarkers, in conjunction with other readily available determinants of outcome assessed in the acute period after injury, add value in the early prognostication of patients with mTBI. Our findings are consistent with the notion that S100B and NSE point to biological mechanisms underlying poor outcome after mTBI.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".