743 Virtual burn care - friend or foe? A systematic review
Bibliographic record
Abstract
Abstract Introduction Interest in virtual care has grown, but evidence surrounding its use for burn injuries is variable. This systematic review assesses the impact of virtual burn care in the past decade (2010-2020) by providing an overview of recent advances in the field. Data on efficacy, feasibility, cost-effectiveness, usability, pros/cons, satisfaction/acceptability, clinical outcomes, and triage effects are presented. Conclusions on its post-pandemic sustainability are drawn. Methods A systematic review with qualitative synthesis was performed according to PRISMA guidelines. Quality of included studies was assessed by validated tools. CINAHL, OVID MEDLINE, APA PsycINFO, and the CENTRAL trials registry were searched. Grey literature was searched for in OAIster, Duck Duck Go, Bandolier Knowledge, LILACS and McMaster Health Systems Evidence. Primary literature published between 01/01/2010-12/31/2020 investigating any of the noted outcomes of interest was retrieved for data extraction. Results A total of 486 studies were identified for screening. 412 and 26 citations were excluded in title/abstract and full text screening, respectively. After removing 8 unretrievable works and 3 straggling duplicates, 50 citations were included. Most works were published from 2016-2020 (n=35, 70%). The most common uses (with some overlap) were acute assessment (n=35, 70%), remote follow-up (n=18, 36%) and tele-rounding (n=4, 8%). Remote photographic burn size (not depth) estimation was found feasible and acceptably accurate. Patient and provider satisfaction was high overall. Patient outcomes with virtual follow-ups were largely comparable to equivalent in-person services, though some adjunct programs saw little benefit. Increased specialist access, more accurate assessment/triage and saved travel time/cost were commonly noted. Challenges included logistics and language barriers for international interventions, IT issues and internet access limitations, HIPAA compliance and some wound/scar assessment challenges (e.g. burn depth and scar vascularity). Conclusions Evidence suggests that virtual burn care is largely safe, efficacious and could be feasible for continued use post-COVID-19 provided technological infrastructure is attainable and suitable regulation exists. Virtual acute specialist burn assessment is particularly well supported.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".