Clinical utilization of near‐infrared spectroscopy devices for burn depth assessment
Bibliographic record
Abstract
The diagnosis of burn depth is based on a visual assessment and can be subjective. Near-infrared (NIR) spectroscopic devices were used preclinically with positive results. The purpose of this study was to test the devices in a clinical setting using easily identifiable burn wounds. Adult patients with acute superficial and full-thickness burns were enrolled. NIR point spectroscopy and imaging devices were used to collect hemodynamic data from the burn site and an adjacent unburned control site. Oxy-hemoglobin and deoxy-hemoglobin concentrations were extracted from spectroscopic data and reported as oxygen saturation and total hemoglobin. Sixteen patients (n=16) were included in the study with equal numbers in both burn wound groups. Point spectroscopy data showed an increase in oxygen saturation (p<0.0095) and total hemoglobin (<0.0001) in comparison with the respective control areas for superficial burn wounds. The opposite was true for full-thickness burns, which showed a decrease in oxygenation (p<0.0001) and total hemoglobin (p<0.0147) in comparison with control areas. NIR imaging technology provides an estimate of hemodynamic parameters and could easily distinguish superficial and full-thickness burn wounds. These results confirm that NIR devices can successfully distinguish superficial and full-thickness burn injuries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".