Factors Associated With Successful MRI Scanning in Unsedated Young Children
Bibliographic record
Abstract
Introduction. Young children are often unable to remain still for magnetic resonance imaging (MRI), leading to unusable images. Various preparation methods may increase success, though it is unclear which factors best predict success. Here, in a retrospective sample, we describe factors associated with successful scanning in unsedated young children. We hypothesized that the mock scanner training and fewer behavior problems would result in higher success rates. Methods. We recruited 134 children aged 2.0-5.0 years for an MRI study. We compared success between children whose parents opted for mock scanner training (n=20) or not (n=114), and evaluated demographic and cognitive factors that predicted success. Results. 97 children (72%) completed at least one MRI sequence successfully on their first try; 64 children (48%) provided high-quality data for all 3 structural imaging sequences. Cognitive scores were higher in successful than unsuccessful children. Children who received mock scanner training were no more likely to be successful than children without, though they had slightly higher scores on T1 image quality. Conclusions. Our data shows that scanning with minimial preparation is possible in young children, and suggests limited advantages of mock scanner preparation for healthy young children. Cognitive ability may predict success.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".