Prognostic Value of S-100β Protein for Prediction of Post-Concussion Symptoms after a Mild Traumatic Brain Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to determine the prognostic value of S-100β protein to identify patients with post-concussion symptoms after a mild traumatic brain injury (mTBI). A search strategy was submitted to seven databases from their inception to October 2016. Individual patient data were requested. Cohort studies evaluating the association between S-100β protein level and post-concussion symptoms assessed at least seven days after the mTBI were considered. Outcomes were dichotomized as persistent (≥3 months) or early (≥7 days <3 months). Our search strategy yielded 23,298 citations of which 29 studies including between seven and 223 patients (n = 2505) were included. Post-concussion syndrome (PCS) (16 studies) and neuropsychological symptoms (9 studies) were the most frequently assessed outcomes. The odds of having persistent PCS (odds ratio [OR] 0.62, 95% confidence interval [CI]: 0.34–1.12, p = 0.11, I 2 0% [n = five studies]) in patients with an elevated S-100β protein serum level were not significantly different from those of patients with normal values while the odds of having early PCS (OR 1.67, 95% CI: 0.98–2.85, p = 0.06, I 2 38% [n = five studies]) were close to statistical significance. Similarly, having an elevated S-100β protein serum level was not associated with the odds of returning to work at six months (OR 2.31, 95% CI: 0.50–10.64, p = 0.28, I 2 22% [n = two studies]). Overall risk of bias was considered moderate. Results suggest that the prognostic biomarker S-100β protein has a low clinical value to identify patients at risk of persistent post-concussion symptoms. Variability in injury to S-100ß protein sample time, mTBI populations, and outcomes assessed could potentially explain the lack of association and needs further evaluation.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".