Nonpharmacological Interventions to Reduce Sedation and General Anesthesia in Pediatric MRI: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Nonpharmacological strategies are increasingly used in pediatric procedures, but in pediatric MRI, sedation and general anesthesia are still commonly required. OBJECTIVES: To evaluate the effectiveness of nonpharmacological interventions in reducing use of sedation and general anesthesia in pediatric patients undergoing MRI, and to investigate effects on scan time, image quality, and anxiety. DATA SOURCES: We searched Ovid Medline, CINAHL, Embase, and CENTRAL from inception through October 10, 2022. STUDY SELECTION: We included randomized controlled trials and quasi-experimental designs comparing the effect of a nonpharmacological intervention with standard care on use of sedation or general anesthesia, scan time, image quality, or child and parental anxiety among infants (<2 years), children, and adolescents (2-18 years) undergoing MRI. DATA EXTRACTION: Standardized instruments were used to extract data and assess study quality. RESULTS: Forty-six studies were eligible for the systematic review. Limited to studies on children and adolescents, the meta-analysis included 20 studies with 33 873 patients. Intervention versus comparator analysis showed that nonpharmacological interventions were associated with reduced need for sedation and general anesthesia in the randomized control trials (risk ratio, 0.68; 95% confidence interval, 0.48-0.95; l2 = 35%) and nonrandomized studies (risk ratio, 0.58; 95% confidence interval, 0.51-0.66; l2 = 91%). The effect was largest among children aged 3 to 10 years when compared with older children and adolescents aged 11 to 18 years. LIMITATIONS: There was substantial heterogeneity among nonrandomized studies. CONCLUSIONS: Nonpharmacological interventions must be considered as standard procedure in infants, children, and adolescents undergoing MRI.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".