Bibliographic record
Abstract
The objective of this study was to determine the incidence of post-burn pruritus in a pediatric population and identify the various treatments used to manage it. This study was initiated as a clinical care review following the introduction of a pruritus scoring system for children known as the “Toronto Pediatric Itch Scale” (TPIS) in 2015. A retrospective review of all patients treated in our pediatric burn program for a burn injury from January 2009 to June 2017 was carried out. Pruritus was categorized as acute (<2 months post-injury) or chronic (>2 months post-injury). TPIS scores were used to record pruritus severity when documented. Relevant demographic variables as well as treatments for pruritus were collected. Patients with pre-existing skin conditions that are associated with pruritus were excluded from the study. Of all patients treated during the study period, 1730 patients met the inclusion criteria. The mean age at injury was 3.8 years (SD, 4.1) and the mean total body surface area (TBSA) of the burn was 3.5% (SD, 5.0). The incidence of acute and chronic pruritus was 35% (95% CI, 0.33–0.37) and 10% (95% CI, 0.83-0.11) respectively. Pruritus was most commonly managed using traditional therapies including massage (73%), diphenhydramine (73%), and hydroxyzine (37%). However, 8% of the study population also received Pulsed Dye and CO2 laser therapies (introduced at our institution in 2014) to treat their chronic pruritus. Following treatment with one session of laser therapy, TPIS scores significantly decreased from 1.49 (SD, 0.68) to 0.73 (SD, 0.84) (p<0.001). Post-burn pruritus was most prevalent in the acute injury phase and was most often managed using conservative treatments. Although laser therapy remains relatively new, it appears to be an effective treatment for post-burn pruritus in children. More specifically, the results from this study demonstrate that laser therapy can significantly improve pruritus after a single treatment. Further research must be carried out in order to determine if laser therapy should be offered as a first-line treatment for chronic pruritus. The information collected in this study contributes to the limited body of literature available regarding pediatric post-burn pruritus. In particular, the findings will allow clinicians to become more knowledgeable regarding the incidence and treatment of pruritus and encourage them to consider offering laser therapy as a treatment option to their pediatric patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".