902 Factors Associated with Burn Center Care for Pediatric Burn Injuries: A Population-based Study
Bibliographic record
Abstract
Abstract Introduction Burns meeting specific clinical criteria should be managed at specialized burn centres. In our province, from 2003 to 2022, six burn centres provided specialized care for pediatric burns. This study aims to identify factors associated with pediatric burn care at these centres and to assess how well the burn centre referral criteria are adhered to. Methods This was a population-based cohort study using linked health administrative datasets. Using hospital discharge records, we identified children (0 to 17 years) with a hospital visit for burn injury between 2003 and 2022. The main exposure was the presence of one or more measurable burn centre referral criteria: 1) burns > 10% TBSA, 2) full thickness burns, 3) burns to special anatomic areas, 4) inhalation, 5) chemical and 6) electrical injuries. We also measured demographic and social vulnerability factors of burned children. The outcome was receipt of treatment at a burn centre. We used standardized differences (SD) to measure differences in management setting by sociodemographic factors and modified Poisson regression to test the association between presence of burn center referral criteria and treatment at a burn centre. Results Of 79,782 children and adolescents with burn injuries, 13,531 (17%) were treated at burn centres. Compared to non-burn centres, burn centres treated a higher proportion of children 0–1-year-old (15% vs. 9%, SD 0.19), 1-4 years old (49% vs. 40%, SD 0.20), and urban residents (94% vs. 77%, SD 0.51). No meaningful differences in treatment setting were observed by sex or by other measures of social vulnerability. Children with at least one referral criterion, compared to those with none, had an increased risk of treatment at a burn centre (1+ referral criterion 7,753/34,812 [22%] burns; no referral criteria 5,998/44,970 [13%] burns; adjusted relative risk [aRR] 1.50, 95% CI 1.46 – 1.54). With increasing numbers of burn centre referral criteria, the risk of treatment at a burn centre increased (referent: no burn centre referral criterion; single criterion 5,398/29,768, aRR 1.27, 95% CI 1.23-1.30; two criteria 1,748/4,459, aRR 2.63, 95% CI 2.51-2.75; three or more criteria 387/585, aRR 4.71, 95% CI 4.32-5.15). Conclusions Compared to non-burn centres, burn centres treat a higher proportion of young individuals, and urban residents. Treatment at a burn centre was low regardless of the presence of one or more burn centre referral criteria and 1.5 times higher if a burn centre referral criterion was present. Applicability of Research to Practice Understanding health system, hospital and clinician barriers and facilitators to aligning burn care delivery with patient needs is important to ensure system efficiency and optimal patient outcomes. Funding for the Study This study was funded by a Studentship Award.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".