Time efficient preparation methods for MRI brain scanning in awake young children and factors associated with success
Bibliographic record
Abstract
Abstract Objective Young children are often unable to remain still for magnetic resonance imaging (MRI). Various preparation methods have been reported to avoid sedation or anesthesia, with mixed success rates and feasibility. Here we describe a time-efficient preparation method and factors associated with successful scanning in young chdilren. We recruited 134 children aged 2.0–5.0 years for an MRI study. Some children completed a training session on a mock scanner, and all children received a 15–20 minute introduction to scanning procedures immediately before their scan. We compared success between children receiving mock scanner training or not, and evaluated demographic or cognitive factors that predicted success. Results 97 children (72%) completed at least one sequence successfully; 64 children provided high-quality data for all 3 sequences. Cognitive scores were higher in successful children, but children who received mock scanner training were less likely to be successful. A case-controlled comparison of children matched on age, gender, and cognitive scores found no differences between children receiving training or not. We present a quick method for preparing young children for awake MRI scans. Our data suggests limited advantages of mock scanner preparation for healthy young children, and that cognitive abilities may help 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".