MR Imaging at 3.0 T in Children: Technical Differences, Safety Issues, and Initial Experience
Bibliographic record
Abstract
The high signal-to-noise ratio and contrast-to-noise ratio of 3.0-T magnetic resonance (MR) imaging can be used to obtain high-resolution thin-section images in a short acquisition time. These advantages are associated with an increased specific absorption rate (SAR) and more artifacts owing to B(1) inhomogeneity and increased susceptibility and chemical shift. Potential advantages of 3-T imaging in children include acquisition of good-quality images even with a small field of view (FOV). The shorter overall acquisition time of 3-T imaging is useful in children, who may not be able to cooperate for long. Shorter acquisition times also improve safety by reducing patient monitoring time within the enclosed bore of an MR imaging unit. SAR-related issues and dielectric artifacts are less problematic with a small FOV. Parallel imaging helps reduce SAR, susceptibility artifacts, and blurring of T2-weighted fast spin-echo (FSE) and single-shot FSE images by reducing the echo train length.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".