Anesthetic Practices for Laser Rehabilitation of Pediatric Hypertrophic Burn Scars
Bibliographic record
Abstract
The use of ablative fractional carbon dioxide laser therapy and pulsed dye laser therapy has led to significant improvements in the rehabilitation of hypertrophic burn scars. However, laser procedures are associated with appreciable pain among pediatric patients. Clinical consensus suggests using general anesthesia for pediatric laser procedures; however, guidelines for perioperative care are lacking. The objective of this quality improvement study is to determine whether a difference exists in postoperative pain outcomes in pediatric patients who receive intraoperative opioid regimens compared with patients who receive opioid-sparing regimens for laser therapy of hypertrophic burn scars. A retrospective review of patients who received laser therapy at a pediatric burn center from April 2014 to May 2015 was performed. Overall, 88 of the 92 procedures reviewed were included. A statistically significant difference was not found between the likelihood of postoperative pain when intraoperative opioid regimens (n = 63) were given compared with opioid-sparing regimens (n = 25) X (1, n = 88) = 2.870, P = .0902. There was also no difference between short-acting (n = 48), long-acting (n = 9), or combination (n = 6) intraoperative opioids compared with opioid-sparing regimens (n = 25) in the likelihood of postoperative pain. Despite the small sample size, the low number of postoperative pain cases is encouraging. Ultimately, these data provide a foundation for developing anesthetic guidelines for pediatric laser procedures. Specifically, clinicians should consider the potential to deliver adequate perioperative care via an opioid-sparing regimen ± adjuvant.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".