ABSTRACT 327
Bibliographic record
Abstract
Background and aims: Mild traumatic brain injuries (TBIs) represent the majority of TBIs in children. Diagnosis and determining a prognosis are often difficult especially in non-verbal children. Aims: The purpose of our systematic review was to evaluate the diagnostic and prognostic value of serum biomarkers in children with mild TBI. Methods: A systematic search of the literature was performed with MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials databases using terminology selected for biomarkers, TBI, and children. Articles were excluded if they did not include children with mild TBI and biomarkers measured within 24 hours of injury. Qualitative assessment was performed using QUADAS-2 and REMARK. Results: Of 5978 articles identified, 29 articles met inclusion criteria. Among 29 studies, only 7 focused exclusively on children. The median sample size was 99 [range 16–1560]. Of 55 biomarkers examined, 22 studies (76%) examined S100B. S100B, white blood cell, neuroinflammation markers, and cellular adhesion molecules were changed significantly in TBI children compared to controls. S100B showed high sensitivity (83–100%) but low specificity (12–65%) in its diagnostic capacity to detect abnormal head CT findings. The prognostic value of the biomarkers evaluated was not conclusive for mild TBI. Also the overall quality of the studies was not generally high and most of them contained potential bias. Conclusions: What is needed: High quality studies of novel serum biomarkers that are more specific for diagnosis and are prognostic in children with mild TBI.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.484 | 0.217 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".