Guidelines for the Management of Pediatric Severe Traumatic Brain Injury, Third Edition: Update of the Brain Trauma Foundation Guidelines
Bibliographic record
Abstract
Severe Traumatic Brain Injury in Infants, Children, and Adolescents in 2019: Some Overdue Progress, Many Remaining Questions, and Exciting Ongoing Work in the Field of Traumatic Brain Injury Research In this Supplement to Pediatric Critical Care Medicine, we are pleased to present the Third Edition of the Guidelines for the Management of Pediatric Severe Traumatic Brain Injury (TBI). This body of work updates the Second Edition of the guidelines that was published in 2012 (1). It represents a substantial effort by a multidisciplinary group of individuals assembled to reflect the team approach to the treatment of these complex, critically ill patients that is essential to optimizing critical care and improving outcomes. This work also represents the strong and always-evolving partnership between investigators from the medical and research communities, forged in Chicago in 2000, from which the first pediatric TBI guidelines were developed. The mutual trust and respect we share have been the foundation of our commitment to bringing evidence-based care to children with TBI. Updating these guidelines was particularly exciting to the individuals who have participated in the previous two editions because several new studies have been published which begin to address a number of major gaps in the pediatric TBI literature—gaps that were specifically identified as targets for future research in earlier editions. For example, we are now able to include reports on the effects of commonly used sedatives and analgesics on intracranial pressure (ICP). Similarly, initial head-to-head comparisons of the influence of agents in routine “real world” use such as hypertonic saline (HTS), fentanyl, and others now inform these guidelines (2,3). A total of 48 new studies were included in this Third Edition. Although some progress has been made and should be celebrated, overall the level of evidence informing these guidelines remains low. High-quality randomized studies that could support level I recommendations remain absent; the available evidence produced only three level II recommendations, whereas most recommendations are level III, supported by low-quality evidence. Based in part on a number of requests from the readership to individual clinical investigators, we have included a companion article in the regular pages of Pediatric Critical Care Medicine that presents a “Critical Pathway” algorithm of care for both first-tier and second-tier (refractory intracranial hypertension) approaches. The algorithm reflects both the evidence-based recommendations from these guidelines and consensus-based expert opinion, vetted by the clinical investigators, where evidence was not available. An algorithm was provided in the First but not Second Editions of the guidelines, and we believe that given the new reports available, along with the existing gaps in evidence, a combination of evidence-based and consensus-based recommendations provides additional and much-needed guidance for clinicians at the bedside. The algorithm also addresses a number of issues that are important but were not previously covered in the guidelines, given the lack of research and the focus on evidence-based recommendations. This includes addressing issues such as a stepwise approach to elevated ICP, differences in tempo of therapy in different types of patients, scenarios with a rapidly escalating need for ICP-directed therapy in the setting of impending herniation, integration of multiple monitoring targets, and other complex issues such as minimal versus optimal therapeutic targets and approaches to weaning therapies. We hope that the readership finds the algorithm document helpful, recognizing that it represents a challenging albeit important step. Designing and developing this pediatric TBI evidence-based guidelines document required an expert administrative management team, and to that end, we are extremely grateful to the staff of the Pacific Northwest Evidence-based Practice Center, Oregon Health & Science University, for their vital contribution to this work. We are also grateful to the Brain Trauma Foundation and the Department of Defense for supporting the development and publication of these guidelines documents. We are grateful to the endorsing societies for recognizing the importance of this work and for the considerable work of the clinical investigators in constructing the final document. 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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".