Ventilatory strategy during liver transplantation: implications for near-infrared spectroscopy-determined frontal lobe oxygenation
Bibliographic record
Abstract
BACKGROUND: As measured by near infrared spectroscopy (NIRS), cerebral oxygenation (ScO2) may be reduced by hyperventilation in the anhepatic phase of liver transplantation surgery (LTx). Conversely, the brain may be subjected to hyperperfusion during reperfusion of the grafted liver. We investigated the relationship between ScO2 and end-tidal CO2 tension (EtCO2) during the various phases of LTx. METHODS: In this retrospective study, 49 patients undergoing LTx were studied. Forehead ScO2, EtCO2, minute ventilation (VE), and hemodynamic variables were recorded from the beginning of surgery through to the anhepatic and reperfusion phases during LTx. RESULTS: In the anhepatic phase, ScO2 was reduced by 4.3% (95% confidence interval: 2.5-6.0%; P < 0.0001), EtCO2 by 0.3 kPa (0.2-0.4 kPa; P < 0.0001), and VE by 0.4 L/min (0.1-0.7 L/min; P = 0.0018). Conversely, during reperfusion of the donated liver, ScO2 increased by 5.5% (3.8-7.3%), EtCO2 by 0.7 kPa (0.5-0.8 kPa), and VE by 0.6 L/min (0.3-0.9 L/min; all P < 0.0001). Changes in ScO2 were correlated to those in EtCO2 (Pearson r = 0.74; P < 0.0001). CONCLUSION: During LTx, changes in ScO2 are closely correlated to those of EtCO2. Thus, this retrospective analysis suggests that attention to maintain a targeted EtCO2 would result in a more stable ScO2 during the operation.
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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) | 0.000 | 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".