Quality Initiatives: Guidelines for Use of Medical Imaging during Pregnancy and Lactation
Bibliographic record
Abstract
The use of computed tomography (CT) and magnetic resonance (MR) imaging has increased tremendously in the past 2 decades. Hence, pregnant and breast-feeding women, although generally healthier than the population at large, are also more likely to require contrast material-enhanced imaging. When a contrast-enhanced CT or MR imaging study is being considered for a pregnant or lactating patient, the potential risks to the fetus related to exposure to radiation, high magnetic fields, or contrast agents must be considered and weighed carefully against the risks of potential misdiagnosis due to withholding contrast agents and imaging studies. Fetal radiation doses up to 1 mGy are considered acceptable; with larger doses, the risk of carcinogenesis approximately doubles, although it remains low in absolute terms. No damage to a developing human fetus caused by MR imaging exposure has been documented. However, caution is advised, and risks and benefits must always be considered before evaluating a pregnant patient with MR imaging. The use of iodinated contrast agents is generally safe during pregnancy; nevertheless, these agents should be used with caution due to the risk of fetal hypothyroidism and should be administered only when the clinical situation clearly requires doing so. The use of gadolinium-based contrast agents during pregnancy remains controversial due to lack of human clinical data and potential toxicity. Use of all contrast agents is considered safe during lactation. It is hoped that this knowledge will help radiologists develop a consensus with their clinical colleagues regarding case management of pregnant and lactating patients.
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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 | 0.041 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".