Pruritus in the Pediatric Burn Population
Bibliographic record
Abstract
Postburn pruritus is a significant issue that can have a devastating impact on patient quality of life. Despite its known negative impact, few studies have focused on the pediatric population. Thus, the aim of this study was to determine the incidence of pruritus among pediatric burn patients as well as identify its predictive factors and commonly used treatments, including the novel use of laser therapy. A retrospective analysis of all burn patients treated at our pediatric burn center from 2009 to 2017 was conducted. The primary outcome measure was the presence or absence of pruritus at any point following the burn. One thousand seven hundred and eighty-three patients met the inclusion criteria for this study. The mean age at injury was 3.67 years (SD = 4.02) and the mean burn TBSA was 3.48% (SD = 4.81) with most burns resulting from scalds (66%). In total, 665 patients (37.3%) experienced pruritus. Following multivariable logistic regression, TBSA, age >5 years, burns secondary to fire/flame, and burn depth, were identified as significant predictors of pruritus (P < .05). Pruritus was treated with diphenhydramine (85.0%), hydroxyzine (37.3%), and gabapentin (4.2%) as well as massage (45.7%), pressure garments (20.0%), and laser therapy (8.6%). This study addresses the knowledge gap in the literature related to postburn pruritus among pediatric patients and includes one of the largest patient cohorts published to date. Moreover, the results further contribute to our understanding of postburn pruritus in children and may help us to predict which patients are most likely to be affected, so that treatment can be initiated as soon as possible.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".