Anesthesia can be safely provided for children in a high‐field intraoperative magnetic resonance imaging environment
Bibliographic record
Abstract
OBJECTIVES: To describe the challenges associated with providing safe anesthesia and perioperative care for children in a remote intraoperative magnetic resonance (iMR) operating room (OR) and to identify perioperative anesthesia outcomes, including adverse events related to the iMR environment. BACKGROUND: Increasingly, children undergo neurosurgical procedures in a high-field iMR OR. We describe a 10-year experience of providing anesthesia for children in this environment with a mobile 1.5-Tesla magnet. METHODS: A 10-year retrospective analysis was conducted of children who underwent neurosurgical procedures in a high-field mobile iMR OR. Primary outcomes related to perioperative adverse events and recovery profiles. Results were expressed as mean ± sd or median (range), as appropriate. RESULTS: One hundred and five procedures were performed on 98 children, aged 4 months-18 years, weighing 6-112 kg. The commonest two diagnostic categories were tumor (n = 52) and seizures (n = 27). Median anesthetic time was 439 (185-710) mins. There were no significant adverse events related to the iMR environment. The mean postanesthetic care unit admission temperature was 37 ± 0.9°C and the mean modified Aldrete Score at 30 mins was 7.2 ± 0.9. Two patients experienced seizures in the immediate postoperative period, readily controlled with propofol. There was one breach of MR safety protocol, and no adverse events related to patient transport. CONCLUSIONS: Anesthesia and perioperative care of children in an iMR setting were associated with a very low incidence of complications, despite the duration of the procedures involved. Such success depends upon a cohesive team-based approach.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".