Pediatric Moderate and Severe Traumatic Brain Injury: A Systematic Review of Clinical Practice Guideline Recommendations
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of death and disability in children. Many clinical practice guidelines (CPGs) have addressed pediatric TBI in the last decade but significant variability in the use of these guidelines persists. Here, we systematically review CPGs recommendations for pediatric moderate-to-severe TBI, evaluate the quality of CPGs, synthesize the quality of evidence and strength of included recommendations, and identify knowledge gaps. A systematic search was conducted in MEDLINE ® , Embase, Cochrane CENTRAL, Web of Science, and Web sites of organizations publishing recommendations on pediatric injury care. We included CPGs developed in high-income countries from January 2012 to May 2023, with at least one recommendation targeting pediatric (≤ 19 years old) moderate-to-severe TBI populations. The quality of included clinical practice guidelines was assessed using the AGREE II tool. We synthesized evidence on recommendations using a matrix based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. We identified 15 CPGs of which 9 were rated moderate to high quality using AGREE II. We identified 90 recommendations, of which 40 (45%) were evidence based. Eleven of these were based on moderate to high quality evidence and were graded as moderate or strong by at least one guideline. These included transfer, imaging, intracranial pressure control, and discharge advice. We identified gaps in evidence-based recommendations for red blood cell transfusion, plasma and platelet transfusion, thromboprophylaxis, surgical antimicrobial prophylaxis, early diagnosis of hypopituitarism, and mental health mangement. Many up-to-date CPGs are available, but there is a paucity of evidence to support recommendations, highlighting the urgent need for robust clinical research in this vulnerable population. Our results may be used by clinicians to identify recommendations based on the highest level of evidence, by healthcare administrators to inform guideline implementation in clinical settings, by researchers to identify areas where robust evidence is needed, and by guideline writing groups to inform the updating of existing guidelines or the development of new ones.
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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 | 0.029 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".