Classification of burn injuries using near-infrared spectroscopy
Bibliographic record
Abstract
Early surgical management of those burn injuries that will not heal spontaneously is critical. The decision to excise and graft is based on a visual assessment that is often inaccurate but yet continues to be the primary means of grading the injury. Superficial and intermediate partial-thickness injuries generally heal with appropriate wound care while deep partial- and full-thickness injuries generally require surgery. This study explores the possibility of using near-infrared spectroscopy to provide an objective and accurate means of distinguishing shallow injuries from deeper burns that require surgery. Twenty burn injuries are studied in five animals, with burns covering <1% of the total body surface area. Carefully controlled superficial, intermediate, and deep partial-thickness injuries as well as full-thickness injuries could be studied with this model. Near-infrared reflectance spectroscopy was used to evaluate these injuries 1 to 3 hours after the insult. A probabilistic model employing partial least-squares logistic regression was used to determine the degree of injury, shallow (superficial or intermediate partial) from deep (deep partial and full thickness), based on the reflectance spectrum of the wound. A leave-animal-out cross-validation strategy was used to test the predictive ability of a 2-latent variable, partial least-squares logistic regression model to distinguish deep burn injuries from shallow injuries. The model displayed reasonable ranking quality as summarized by the area under the receiver operator characteristics curve, AUC = 0.879. Fixing the threshold for the class boundaries at 0.5 probability, the model sensitivity (true positive fraction) to separate deep from shallow burns was 0.90, while model specificity (true negative fraction) was 0.83. Using an acute porcine model of thermal burn injuries, the potential of near-infrared spectroscopy to distinguish between shallow healing burns and deeper burn injuries was demonstrated. While these results should be considered as preliminary and require clinical validation, a probabilistic model capable of differentiating these classes of burns would be a significant aid to the burn specialist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".