Contrast-enhanced Magnetic Resonance Imaging in Pediatric Patients: Review and Recommendations for Current Practice
Bibliographic record
Abstract
Magnetic resonance imaging (MRI), frequently with contrast enhancement, is the preferred imaging modality for many indications in children. Practice varies widely between centers, reflecting the rapid pace of change and the need for further research. Guide-line changes, for example on contrast-medium choice, require continued practice reappraisal. This article reviews recent developments in pediatric contrast-enhanced MRI and offers recommendations on current best practice. Nine leading pediatric radiologists from internationally recognized radiology centers convened at a consensus meeting in Bordeaux, France, to discuss applications of contrast-enhanced MRI across a range of indications in children. Review of the literature indicated that few published data provide guidance on best practice in pediatric MRI. Discussion among the experts concluded that MRI is preferred over ionizing-radiation modalities for many indications, with advantages in safety and efficacy. Awareness of age-specific adaptations in MRI technique can optimize image quality. Gadolinium-based contrast media are recommended for enhancing imaging quality. The choice of most appropriate contrast medium should be based on criteria of safety, tolerability, and efficacy, characterized in age-specific clinical trials and personal experience.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".