Simultaneous in‐<i>situ</i> NIRS of liver and bowel during septic shock
Bibliographic record
Abstract
We wished to use near infrared spectroscopy (NIRS) to evaluate changes in blood perfusion in the liver and small bowel. However, conventional clinical NIRS probes use adhesive light shielding appliqués that fail in direct contact with the wetness and softness of the liver and small bowel surfaces. We describe our development and testing of customized NIRS probe holders. Methods: Studies were conducted in 11 juvenile (9–17 kg) anesthetized Yorkshire piglets, with intact liver and bowel exposed during experimental septic shock induced by infusion of Escherichia coli. Internally, the emitter and detector of one NIRO‐300 channel were sheathed, directed towards each other, and affixed to the opposite arms of a long‐jawed ratchet clamp applied directly to the liver. The emitter and detector of the NIRO‐300′s second channel were applied, facing each other, on opposite inner surfaces of a semicircular, semi‐rigid, reinforced rubber tube through which a loop of the small bowel was drawn. Externally, optodes from a NIRO‐500 were applied to the skin over the liver. NIRS data were collected at 1second intervals for 180 minutes. Results: A wide range of optical neutral density filters was required to attenuate the emissions because of the diversity of tissue density between subjects. There were no complications with the liver clamp, and the initial tendency for the bowel to slip from the NIRS holder was solved by securing it with surgical ribbon. Conclusion: Since the NIRO‐300 control algorithm did not halt monitoring; the NIRO‐300 data correlated with the NIRO‐500 data (r2=0.292 to 0.659) and both were correlated with the mean arterial blood pressure (range: r2=0.309 to 0.918, p< 0.05 for r2 > 0.105), our modifications were deemed successful.
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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 | 0.000 |
| 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".