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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".