Perioperative anaesthetic concerns during paediatric epilepsy surgeries: A retrospective chart review
Bibliographic record
Abstract
Abstract Background: Epilepsy in children is a common medical condition which usually responds well to antiepileptic drugs (AEDs). Surgical intervention is required in 30-40% of patients for refractory epilepsy. Paediatric patients present challenges to neurosurgeons as well as anaesthesiologists in view of the inherent physiological and developmental differences. We aimed to analyse the key perioperative factors affecting the paediatric epilepsy surgeries as well as the intraoperative and intensive care unit (ICU) complications and safety of the procedure in children. Materials and Methods: We performed a retrospective chart review of perioperative data of 39 patients who underwent surgery for refractory epilepsy. Results: The surgical procedures were either resective or disconnective. The fraction of blood volume lost intraoperatively correlated well with the duration of surgery (P < 0.001, r =0.76). In the postoperative course in ICU, 7 children required postoperative ventilation and 14 developed fever, which was significantly more (P < 0.001) after disconnective surgeries. Conclusions: The blood loss and delayed recovery were found to be the main anaesthetic concerns perioperatively, especially with disconnective surgeries. The choice of anaesthetic agents did not affect electrocorticography or the course of surgery. Neurological complications and fever of non-infectious aetiology must be considered in the postoperative period in ICU. Paediatric epilepsy surgery can be safe and feasible with multidisciplinary team 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".