Whole-Body MRI in Children: Concepts and Controversies—<i>AJR</i> Expert Panel Narrative Review
Bibliographic record
Abstract
The use of whole-body MRI (WBMRI) in children, from infancy to adolescence, has expanded rapidly over the past decade, with increasing uptake and a broadening range of clinical indications. Current indications include screening for presymptomatic lesions in cancer predisposition syndromes; tumor staging in known malignancies; investigating fevers of unknown origin; as well as diagnosing and monitoring rheumatologic diseases, vascular anomalies, and neuromuscular disorders. This AJR Expert Panel Narrative Review aims to offer a comprehensive discussion of WBMRI in pediatric patients, exploring protocols and other technical considerations, clinical indications, implementation challenges and troubleshooting, as well as controversies in widespread adoption, while considering emerging trends and directions. Commonalities and variations in WBMRI protocols across indications and institutions are presented, highlighting the need for greater standardization. Barriers to WBMRI access, particularly in resource-limited settings, are considered, along with potential solutions. The available evidence regarding potential patient benefit from WBMRI across various applications is summarized.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".