Assessing Pediatric Burn Wound Infection Using a Point-of-Care Fluorescence Imaging Device
Bibliographic record
Abstract
Wound infection is the most common complication among pediatric burn patients. When not treated promptly, burn wound infection may lead to delayed healing, failure of skin grafts, or death. Standard burn wound assessment includes inspection for visual signs and symptoms of infection (VSSI) and microbial sampling. To aid in the assessment of burn wound infection, the MolecuLight, a point-of-care autofluorescence imaging device, was introduced at our pediatric burn program in 2020. The MolecuLight uses violet light to illuminate the wound bed, causing clinically relevant quantities of 29 different species of bacteria (>104 CFU/g) to fluoresce in real time. The objectives of this study were to evaluate the role of the MolecuLight in the management of pediatric burn wounds and determine if the findings from the MolecuLight corresponded to VSSI and/or microbial sampling. A retrospective review of patients 0-18 years who had burn wounds assessed with the MolecuLight between November 1, 2020 and June 8, 2023 was conducted. Data were extracted from the medical records of 178 eligible patients with 218 wounds imaged with the device. Fluorescence corresponded with VSSI in 81% of wounds and microbial findings in 82% of wounds. MolecuLight fluorescence, in combination with VSSI, improved sensitivity for detecting wound infections by 39% and decreased specificity by 19% compared to visual signs and symptoms in isolation. Incorporation of the MolecuLight in standard burn wound assessments can improve the detection of infections, which may promote improved wound healing outcomes and antimicrobial stewardship.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".