039 Determination of Burn Depth using near Infrared Spectroscopy
Bibliographic record
Abstract
Introduction: Burn depth, based on the hemodynamic alterations that occur following a thermal insult, can be assessed in a rapid, non‐invasive, and nondestructive fashion using near infrared (NIR) spectroscopy. NIR has the capability to determine the difference between superficial and full thickness burn injuries. Methods: Sixteen burn patients admitted to an adult regional burn center were studied and evaluated with the NIR point and imaging devices. Non‐burned skin adjacent to the burn site was used as the control. NIR measurements were compared between superficial (8 wounds), full thickness (8 wounds) burn wounds and control sites. Results: NIR was able to easily detect an increase in oxyhemoglobin (68.3%, p < 0.05), oxygen saturation (4.8%, p < 0.05%) and total hemoglobin (91.3%, p < 0.05) which typically occurs with superficial burn injuries. Full thickness injuries experienced a substantial drop in oxyhemoglobin (88.8%, p < 0.05), oxygen saturation (79.1%, p < 0.05) and total hemoglobin (77.5%, p < 0.05) in comparison to control sites. Conclusions: These results confirm that NIR spectroscopy can successfully distinguish between superficial and full thickness burn injuries. The second phase of this study will involve determining the depth of indeterminant burn wounds and this preliminary data will also be presented. Acknowledgement: National Research Council of Canada
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".