Clinical management and functional neuromonitoring in traumatic brain injury in children
Bibliographic record
Abstract
PURPOSE OF REVIEW: Traumatic brain injury is the main cause of childhood disability and death. In this review, we highlight recent original findings and emerging themes from published literature on children with serious traumatic brain injury. RECENT FINDINGS: We focus this review on lessons learned from our recent randomized clinical trial of hypothermia therapy in severe traumatic brain injury in children and on bedside neuromonitoring. We propose that integrating the measurement of biomarkers into clinical care as surrogate endpoints and as potential prognostic markers would allow us to evaluate earlier the effect of injury and clinical care in children after traumatic brain injury. Several methods are now more readily available to monitor cerebral physiology in children. These methods include indices evaluating the integrity of cerebral autoregulation, such as the pressure reactivity index derived from values obtained from intracranial pressure measurements, flow velocity measurements from transcranial Doppler ultrasonography or from cerebral oximetry. Other methods allow the evaluation of coma with the nonlinear analysis of electroencephalography or the evaluation of cerebral metabolism and cell death pathways with biomarkers from serum, cerebral spinal fluid, and cerebral microdialysis. SUMMARY: We suggest expanding clinical functional neuromonitoring to help clinicians understand the burden of exposure to physiological variables and response to therapies during intensive care in order to enhance the management of critically ill children with traumatic 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".