Clinical Practice Guideline Recommendations in Pediatric Orthopaedic Injury: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Lack of adherence to recommendations on pediatric orthopaedic injury care may be driven by lack of knowledge of clinical practice guidelines (CPGs), heterogeneity in recommendations or concerns about their quality. We aimed to identify CPGs for pediatric orthopaedic injury care, appraise their quality, and synthesize the quality of evidence and the strength of associated recommendations. METHODS: We searched Medline, Embase, Cochrane CENTRAL, Web of Science and websites of clinical organizations. CPGs including at least one recommendation targeting pediatric orthopaedic injury populations on any diagnostic or therapeutic intervention developed in the last 15 years were eligible. Pairs of reviewers independently extracted data and evaluated CPG quality using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. We synthesized recommendations from high-quality CPGs using a recommendations matrix based on the GRADE Evidence-to-Decision framework. RESULTS: We included 13 eligible CPGs, of which 7 were rated high quality. Lack of stakeholder involvement and applicability (i.e., implementation strategies) were identified as weaknesses. We extracted 53 recommendations of which 19 were based on moderate or high-quality evidence. CONCLUSIONS: We provide a synthesis of recommendations from high-quality CPGs that can be used by clinicians to guide treatment decisions. Future CPGs should aim to use a partnership approach with all key stakeholders and provide strategies to facilitate implementation. This study also highlights the need for more rigorous research on pediatric orthopaedic trauma. LEVEL OF EVIDENCE: Level II-therapeutic study.
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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 | 0.034 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| 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".