Evaluating the health of compromised tissues using a near-infrared spectroscopic imaging system in clinical settings: lessons learned
Bibliographic record
Abstract
The present and accepted standard for determining the status of tissue relies on visual inspection of the tissue. Based on the surface appearance of the tissue, medical personnel will make an assessment of the tissue and proceed to a course of action or treatment. Visual inspection of tissue is central to many areas of clinical medicine, and remains a cornerstone of dermatology, reconstructive plastic surgery, and in the management of chronic wounds, and burn injuries. Near infrared spectroscopic imaging holds the promise of being able to monitor the dynamics of tissue physiology in real-time and detect pathology in living tissue. The continuous measurement of metabolic, physiological, or structural changes in tissue is of primary concern in many clinical and biomedical domains. A near infrared hyperspectral imaging system was constructed for the assessment of burn injuries and skin flaps or skin grafts. This device merged basic science with engineering and integrated manufacturing to develop a device suitable to detect ischemic tissue. This device has the potential of providing measures of tissue physiology, oxygen delivery and tissue hydration during patient screening, in the operating room or during therapy and post-operative/treatment monitoring. Results from a pre-clinical burn injury study will be presented.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".