Anesthesia for pediatric magnetic resonance imaging: a review of practices and current pathways
Bibliographic record
Abstract
PURPOSE OF REVIEW: Magnetic resonance imaging (MRI) is an ever-expanding investigation modality in children. This review aims to present current strategies to perform MRI in pediatrics efficiently and safely. The latest evidence on approaches, safety and costs of MRI with no sedation or with sedation provided by anesthesiologists and non-anesthesiologists are outlined and discussed. RECENT FINDINGS: MRI under sedation provided by either anesthesiologists or non-anesthesiologists has a low incidence of minor adverse events and rarely severe complications. Propofol infusion with or without dexmedetomidine appears the ideal anesthetic, as it allows spontaneous breathing and fast turnover. Intranasal dexmedetomidine is safe and the most effective medication when a nonintravenous route is employed.New scanning techniques and patient's preparation methods can increase the chances to successfully perform MRI with no sedation by shortening sequences, reducing artifacts, and improving child's cooperation. SUMMARY: MRI under sedation can be considered safe. Proper patient selection, clear decision-making and medico-legal pathways are particularly necessary for nurse-only sedated scans. Nonsedated MRIs are feasible and cost-effective but require optimal scanning techniques and patient's preparation to be successful. Further research should be focused on identifying the most effective modalities to perform MRI without sedation and clarify protocols for the nurse-only sedations.Anesthesia service will likely remain pivotal for complex and critically ill patients and to provide assistance in case of adverse events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".