Combined cerebral and somatic near-infrared spectroscopy oximetry monitoring during liver surgery: an observational and non-interventional study
Bibliographic record
Abstract
BACKGROUND: Cerebral oximetry using near-infrared spectroscopy (NIRS) is used for monitoring cerebral oxygen saturation during cardiac surgery and is correlated with clinical outcomes. Our goal was to explore cerebral and somatic NIRS in liver resections as a predictor of post-operative complications. METHODS: Prospective observational and non-interventional study from a tertiary care university hospital including adult patients undergoing liver resection monitored using NIRS at four sites before and during surgery. Those sites were: frontotemporal left and right zones, right thigh, and right arm. Anesthesiologists and surgeons were blinded to oximetry values. Correlations were assessed between baseline oximetry values and cerebro-somatic desaturation load (threshold of 80% from baseline) values with peri-operative events and complications. RESULTS: Ninety patients were distributed equally among gender with a mean age of 59.7 ± 13.1 years. Lower baseline cerebral and/or somatic values were associated with increased risk of delirium, respiratory failure, surgical and renal complications, blood transfusions, and length of stay in the intensive care unit and in the hospital (P < 0.05). The severity of somatic desaturation below 80% was the only parameter associated with blood losses (P = 0.030) and length of hospital stay (P = 0.047). CONCLUSIONS: Cerebral and somatic desaturation does occur in liver resection and can be used simultaneously during liver surgery. Both baseline cerebral and somatic NIRS values are correlated with complications and outcomes. However, thigh desaturation appears more sensitive than cerebral NIRS values in predicting some of these complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".