Adherence of Burn Outpatient Clinic Referrals to ABA Criteria in a Tertiary Center: Creating Unnecessary Referrals?
Bibliographic record
Abstract
Initial assessment and triage of burns are guided by the American Burn Association criteria for referral to a burn center. These criteria are sensitive but not specific and can potentially lead to over-triage and "unnecessary" clinic visits. We are a Level 1 trauma center with burn subspecialty care, and due to the COVID-19 pandemic, referrals to our multidisciplinary outpatient burn clinic required triaging for virtual care appointments. In order to improve the triage process, we retrospectively reviewed our outpatient burn clinic referrals over a 2-year period, 2018 to 2019, for adherence to American Burn Association criteria. We collected data pertaining to patient and burn characteristics, as well as treatment outcome, to characterize referrals not requiring an in-person appointment. Of the 244 patients referred, 73% met the referral criteria, with 45% of these patients being healed at the first visit and 14.6% requiring surgical management. Mean time from injury to first visit was 9.7 days (mode 6), and the average number of visits was 2. Overall, mean burn size was 2%, with the majority of injuries being partial thickness (71%), located in the hand or extremity (77%). There was a fairly equal distribution of contact (36%), flame (21%), and scald (26%) burns. This study highlights the nonspecific nature of the American Burn Association referral criteria. We found that pediatric and hand burns in particular were over-triaged and lead to "unnecessary" appointments. This information is useful to help adjust referral criteria and to guide triaging of appointments with the evolution of telehealth and virtual care.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".