ISUOG Guidelines for fetal MRI: a response to 3‐T fetal imaging and limited fetal exams
Bibliographic record
Abstract
ISUOG Guidelines for fetal MRI: a response to 3-T fetal imaging and limited fetal examsWe are writing in reference to the recently published ISUOG guidelines for the performance of fetal magnetic resonance imaging (MRI) 1 , which we applaud as an important tool for healthcare practitioners.In the section entitled 'How should fetal MRI be performed?',the merits of 1.5 Tesla (T) vs 3 T are discussed and it is stated that 'higher field strength (i.e. 3 T) is currently not recommended for in-vivo fetal imaging'.We would like to express our disagreement with this statement.As its only reference of support, the guideline cites a paper from the Society of Obstetricians and Gynecologists in Canada, which in fact endorses the use of 3-T MRI, as follows: 'fetal MRI is safe at 3.0 Tesla or less during the 2 nd and 3 rd trimesters' 2 .This Canadian guideline is recognized by the Agency for Health Research and Quality (AHRQ) as an evidence-based guideline.The AHRQ is a branch of the United States Department of Health and Human Sciences, charged with improving the safety and quality of America's healthcare system 3 .The theoretical advantages of 3-T fetal imaging are recognized in the ISUOG guidelines, which state that '3 T has the potential to provide imaging with higher resolution and better signal-to-noise ratio than does 1.5 T, while maintaining a comparable or lower energy deposition', as illustrated in Figure 1.In fact, the higher resolution of 3 T compared with 1.5 T has been demonstrated objectively, in two recent publications from
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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 | 0.047 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.047 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".