Assessment of Skin Flaps Using Optically Based Methods for Measuring Blood Flow and Oxygenation
Bibliographic record
Abstract
The objective of this study was to compare two noninvasive techniques, laser Doppler and optical spectroscopy, for monitoring hemodynamic changes in skin flaps. Animal models for assessing these changes in microvascular free flaps and pedicle flaps were investigated. A 2 x 3-cm free flap model based on the epigastric vein-artery pair and a reversed MacFarlane 3 x 10-cm pedicle flap model were used in this study. Animals were divided into four groups, with groups 1 (n = 6) and 2 (n = 4) undergoing epigastric free flap surgery and groups 3 (n = 3) and 4 (n = 10) undergoing pedicle flap surgery. Groups 1 and 4 served as controls for each of the flap models. Groups 2 and 3 served as ischemia-reperfusion models. Optical spectroscopy provides a measure of hemoglobin oxygen saturation and blood volume, and the laser Doppler method measures blood flow. Optical spectroscopy proved to be consistently more reliable in detecting problems with arterial in flow compared with laser Doppler assessments. When spectroscopy was used in an imaging configuration, oxygen saturation images of the entire flap were generated, thus creating a visual picture of global flap health. In both single-point and imaging modes the technique was sensitive to vessel manipulation, with the immediate post operative images providing an accurate prediction of eventual outcome. This series of skin flap studies suggests a potential role for optical spectroscopy and spectroscopic imaging in the clinical assessment of skin flaps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".