ABSTRACT 326
Bibliographic record
Abstract
Background and aims: Traumatic brain injury (TBI) is the most common cause of death and acquired disabilities in children. To better plan support services for children and their families we need to be able to predict how the TBI will affect their functional outcome. Aims: The objectives of this study were to examine the associations between serum biomarkers and functional outcome in children with TBI. Methods: A prospective multicenter pilot observational study of children (5 to 17-years old) with TBI was conducted to examine the association between 11 serum biomarkers and unfavorable outcome at 12 months post-injury. Blood samples for biomarkers were collected daily.Table: No title available.Results: 30 children who were measured serum biomarkers within 24 hours post-injury were examined, and 5 of them showed unfavorable outcome (Pediatric Cerebral Performance Category change from baseline of 1 or more over 12 months). S100B, MBP, and NSE levels on day 1 were related to outcome. The prognostic values of these biomarkers were high with areas under the receiver operating curves of 0.79 to 0.93. Moreover, the pattern of change of some biomarkers, over several days, was also related to outcome.FigureConclusions: Serum biomarkers may be useful for predicting functional outcome after pediatric 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.685 | 0.563 |
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".