Noninvasive Measurement of Edema in Partial Thickness Burn Wounds
Bibliographic record
Abstract
A lack of noninvasive tools to quantify edema has limited our understanding of burn wound edema pathophysiology in a clinical setting. Near-infrared spectroscopy (NIR) is a new noninvasive tool able to measure water concentration/edema in tissue. The purpose of this study was to determine whether NIR could detect water concentration changes or edema formation in acute partial-thickness burn injuries. Adult burn patients within 72 hours postinjury, thermal etiology, partial-thickness burn depth, and <20% TBSA were included. Burn wounds were stratified into partial-thickness superficial or deep wounds based on histology and wound healing time. NIR devices were used to quantify edema in a burn and respective control sites. The sample population consisted of superficial (n = 12) and deep (n = 5) partial-thickness burn injuries. The patients did not differ with respect to age (40 +/- 15 years), TBSA (5 +/- 4%), and mean time for edema assessment (2 days). Water content increased 15% in burned tissue compared with the respective control regions. There were no differences in water content at the control sites. At 48 hours, deep partial-thickness injuries showed a 23% increase in water content compared with 18% superficial partial-thickness burns. NIR could detect differences in water content or edema formation in partial-thickness burns and unburned healthy regions. NIR holds promise as a noninvasive, portable clinical tool to quantify water content or edema in burn wounds.
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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) | 0.001 | 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".