Magnetic Resonance Imaging in Preterm Infant: A Systematic Review on Clinical Procedure Safety
Bibliographic record
Abstract
BACKGROUND: Currently, there is no evidence that MRI produces harmful effects on premature newborns, as well as short-term and long-term safety issues regarding radiofrequency fields and loud acoustic environment, while the examination that is being performed has not been clearly investigated. MRI of the brain conducted on preterm infants should be part of the diagnostic workup, when necessary. This article is intended to evaluate the short-term safety of MRI performed in preterm infants, when required, by analyzing all vital parameters available before, during, and after the MRI procedures. METHODS: We conducted a systematic review of the literature on electronic medical databases (PubMed and ClinicalTrials.gov) following the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We included all preterm infants who underwent MRI whose clinical, hemodynamic, and respiratory parameters were reported. The quality of the included articles was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. RESULTS: Six studies were included with a total of 311 preterm infants. No severe adverse event, such as death, occurred during MRI procedures. Vital signs remained stable in about two-thirds of all patients. CONCLUSIONS: Given the general clinical safety of MRI, we suggest it as a tool to be used in preterm infants in Neonatal Intensive Care Units, when necessary. We further suggest the development of standard protocols to guide the use of MRI in preterm infants to maximize the clinical safety of the procedure.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".