Management of Pediatric Severe Traumatic Brain Injury: 2019 Consensus and Guidelines-Based Algorithm for First and Second Tier Therapies
Bibliographic record
Abstract
OBJECTIVES: To produce a treatment algorithm for the ICU management of infants, children, and adolescents with severe traumatic brain injury. DATA SOURCES: Studies included in the 2019 Guidelines for the Management of Pediatric Severe Traumatic Brain Injury (Glasgow Coma Scale score ≤ 8), consensus when evidence was insufficient to formulate a fully evidence-based approach, and selected protocols from included studies. DATA SYNTHESIS: Baseline care germane to all pediatric patients with severe traumatic brain injury along with two tiers of therapy were formulated. An approach to emergent management of the crisis scenario of cerebral herniation was also included. The first tier of therapy focuses on three therapeutic targets, namely preventing and/or treating intracranial hypertension, optimizing cerebral perfusion pressure, and optimizing partial pressure of brain tissue oxygen (when monitored). The second tier of therapy focuses on decompressive craniectomy surgery, barbiturate infusion, late application of hypothermia, induced hyperventilation, and hyperosmolar therapies. CONCLUSIONS: This article provides an algorithm of clinical practice for the bedside practitioner based on the available evidence, treatment protocols described in the articles included in the 2019 guidelines, and consensus that reflects a logical approach to mitigate intracranial hypertension, optimize cerebral perfusion, and improve outcomes in the setting of pediatric severe 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 | 0.038 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".