Detecting Intestinal Ischemia Using near Infrared Spectroscopy
Bibliographic record
Abstract
Blood supply to the intestine can suddenly be interrupted. Acute mesenteric intestinal ischemia often requires invasive surgery to restore blood supply to the intestine. Early correction of vascular insufficiency is the most important factor in improving patient survival when confronted with acute mesenteric intestinal ischemia. A prolonged loss of blood flow results in irreversible damage to the intestine that can lead to death. It is also imperative that dead segments of the intestines be removed. Several subjective criteria are relied upon to differentiate viable from non-viable tissue, unfortunately, these criteria can lead to an inaccurate assessment. A porcine model of intestinal ischemia was used to determine the efficacy of using near infrared (NIR) spectroscopy to find ischemic segments of the intestine and detect the onset of reperfusion following resolution of vascular occlusion. Nine segments of intestine were identified and six were assigned to three treatment groups; (1) segments undergoing no vascular manipulations, (2) segments undergoing arterial/venous occlusion and (3) segments undergoing arterial/venous occlusion followed by reperfusion. The remaining segments were used as spacers and interposed between each of the ischemia segments. A classification model, using partial least square discriminant analysis, was built on the spectra collected from the segments with no vascular manipulations and the segments that were solely subjected to arterial/venous occlusion. The spectra collected from the intestinal segments that experienced both occlusion and reperfusion were used to test the classification model. The model was able to detect and distinguish ischemic intestinal tissue with a specificity and sensitivity exceeding 80% with an overall classification accuracy of 89%. The method appears to be well suited as an intra-operative assessment method when intestinal ischemia is a concern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".