Is magnetic resonance imaging teratogenic during pregnancy? Literature review
Bibliographic record
Abstract
Magnetic resonance imaging is a diagnostic tool used for obtaining an image through the combination of electromagnetic fields and radiofrequency. Given its properties and safety, it is the imaging modality of choice in pregnant women. However, little is known about the effects of MRI on the developing foetus. Objectives To identify the effects of the use of magnetic resonance imaging on the foetus when used as a diagnostic tool during pregnancy. Materials and methods A literature search was performed in PubMed, Embase, and LILACS. Clinical guidelines and the grey literature were also reviewed. An analysis was made based on the findings. Results Four potentially adverse effects where found: (1) The impact on the auditory development due to the acoustic sound made by the resonator. (2) Teratogenic effects on DNA. (3) Physical deformities secondary to temperature increase. (4) Teratogenic effects due to the use of gadolinium as a contrast agent. Conclusion The risk assessment on the use of magnetic resonance imaging on the foetus is complex, owing to the multiple differences in field strength, force gradients, and radiofrequency pulses used. Although the adverse effects of using this method are not very clear, there are studies that describe the possible outcomes that can result from the use of this imaging modality. It is recommended to use MRI with caution, as long as the benefits outweigh the risk in pregnant 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".