The Use of Laser Doppler Imaging as a Predictor of Burn Depth and Hypertrophic Scar Postburn Injury
Bibliographic record
Abstract
Hypertrophic scarring (HTS) is a fibroproliferative disorder that commonly develops after severe burn injuries. Overexpression of transforming growth factor-β (TGF-β) by an increased number of fibrocytes has been associated with increased extracellular matrix molecule expression leading to HTS. The most widely accepted adjuvant to clinical assessment of burn depth is laser Doppler imaging (LDI) and may predict injury to the dermis that corresponds to cellular and molecular changes associated with HTS. A prospective, blinded, control trial was performed comparing LDI and clinical assessment for the decision to operate. Immunohistochemistry and real-time reverse transcription polymerase chain reaction was performed to determine whether there is a correlation between histological assessment of burn depth and LDI, and the presence of fibrocytes was detected using confocal microscopy. The positive predictive value for a burn requiring a graft was calculated to be >90%. Immunohistochemistry on biopsy samples revealed an increased expression of TGF-β, connective tissue growth factor, heat shock protein 47, and collagen type I in deep burn wounds compared to superficial burns. Using the fibrocyte-specific markers procollagen type I and lymphocyte-specific protein-1, there was an increased number of fibrocytes in deep burn areas compared to superficial burn. In deep burn injuries, increased infiltration of fibrocytes occurs leading to an overexpression of TGF-β1 and connective tissue growth factor. More importantly, LDI was >90% accurate at predicting the need for excision and grafting. The accuracy of the decision to debride deep dermal burns to avoid HTS using both clinical parameters and LDI was supported by histological and biochemical measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".