Gabapentin as a novel adjunct for postoperative irritability after superior cavopulmonary connection operation in children
Bibliographic record
Abstract
OBJECTIVES: Describing our institution's off-label use of gabapentin to treat irritability after superior cavopulmonary connection surgery and its impact on subsequent opiate and benzodiazepine requirements. METHODS: This is a single-center retrospective cohort study including infants who underwent superior cavopulmonary connection operation between 2011 and 2019. RESULTS: Gabapentin was administered in 74 subjects (74/323, 22.9%) during the observation period, with a median (IQR) starting dose of 5.7 (3.3, 15.0) mg/kg/day and a maximum dose of 10.7 (5.5, 23.4) mg/kg/day. Infants who underwent surgery in 2015-19 were more likely to receive gabapentin compared with those who underwent surgery in 2011-14 (p < 0.0001). Infants prescribed gabapentin were younger at surgery (137 versus 146 days, p = 0.007) and had longer chest tube durations (1.8 versus 0.9 days, p < 0.001), as well as longer postoperative intensive care (5.8 versus 3.1 days, p < 0.0001) and hospital (11.5 versus 7.0 days, p < 0.0001) lengths of stays. The year of surgery was the only predisposing factor associated with gabapentin administration in multivariate analysis. In adjusted linear regression, infants prescribed gabapentin on postoperative day 0-4 (n = 64) had reduced benzodiazepine exposure in the following 3 days (-0.29 mg/kg, 95% CI -0.52 - -0.06, p = 0.01) compared with those not prescribed gabapentin, while no difference was seen in opioid exposure (p = 0.59). CONCLUSIONS: Gabapentin was used with increasing frequency during the study period. There was a modest reduction in benzodiazepine requirements associated with gabapentin administration and no reduction in opioid requirements. A randomised controlled trial could better assess gabapentin's benefits postoperatively in children with congenital heart disease.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".