Clinical Practice Guideline Recommendations For Pediatric Multisystem Trauma Care
Bibliographic record
Abstract
OBJECTIVE: To systematically review clinical practice guidelines (CPGs) for pediatric multisystem trauma, appraise their quality, synthesize the strength of recommendations and quality of evidence, and identify knowledge gaps. BACKGROUND: Traumatic injuries are the leading cause of death and disability in children, who require a specific approach to injury care. Difficulties integrating CPG recommendations may cause observed practice and outcome variation in pediatric trauma care. METHODS: We conducted a systematic review using Medline, Embase, Cochrane Library, Web of Science, ClinicalTrials, and grey literature, from January 2007 to November 2022. We included CPGs targeting pediatric multisystem trauma with recommendations on any acute care diagnostic or therapeutic interventions. Pairs of reviewers independently screened articles, extracted data, and evaluated the quality of CPGs using "Appraisal of Guidelines, Research, and Evaluation II." RESULTS: We reviewed 19 CPGs, and 11 were considered high quality. Lack of stakeholder engagement and implementation strategies were weaknesses in guideline development. We extracted 64 recommendations: 6 (9%) on trauma readiness and patient transfer, 24 (38%) on resuscitation, 22 (34%) on diagnostic imaging, 3 (5%) on pain management, 6 (9%) on ongoing inpatient care, and 3 (5%) on patient and family support. Forty-two (66%) recommendations were strong or moderate, but only 5 (8%) were based on high-quality evidence. We did not identify recommendations on trauma survey assessment, spinal motion restriction, inpatient rehabilitation, mental health management, or discharge planning. CONCLUSIONS: We identified 5 recommendations for pediatric multisystem trauma with high-quality evidence. Organizations could improve CPGs by engaging all relevant stakeholders and considering barriers to implementation. There is a need for robust pediatric trauma research, to support recommendations.
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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 | 0.069 | 0.291 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".