Using near-infrared spectroscopy to measure cerebral metabolic rate of oxygen under multiple levels of arterial oxygenation in piglets
Bibliographic record
Abstract
Improving neurological care of neonates has been impeded by the absence of suitable techniques for measuring cerebral hemodynamics and energy metabolism at the bedside. Currently, near-infrared spectroscopy (NIRS) appears to be the technology best suited to fill this gap, and techniques have been proposed to measure both cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). We have developed a fast and reliable bolus-tracking method of determining CMRO2 that combines measurements of CBF and cerebral venous oxygenation [venous oxygen saturation (CSvO2)]. However, this method has never been validated at different levels of arterial oxygenation [arterial oxygen saturation (SaO2)], which can be highly variable in the clinical setting. In this study, NIRS measurements of CBF, CSvO2, and CMRO2 were obtained over a range of SaO2 in newborn piglets (n=12); CSvO2 values measured directly from sagittal sinus blood samples were collected for validation. Two alternative NIRS methods that measure CSvO2 by manipulating venous oxygenation (i.e., head tilt and partial venous occlusion methods) were also employed for comparison. Statistically significant correlations were found between each NIRS technique and sagittal sinus blood oxygenation (P<0.05). Correlation slopes were 1.03 (r=0.91), 0.73 (r=0.73), and 0.73 (r=0.81) for the bolus-tracking, head tilt, and partial venous occlusion methods, respectively. The bolus-tracking technique displayed the best correlation under hyperoxic (SaO2=99.9±0.03%) and normoxic (SaO2=86.9±6.6%) conditions and was comparable to the other techniques under hypoxic conditions (SaO2=40.7±9.9%). The reduced precision of the bolus-tracking method under hypoxia was attributed to errors in CSvO2 measurement that were magnified at low SaO2 levels. In conclusion, the bolus-tracking technique of measuring CSvO2, and therefore CMRO2, is accurate and robust for an SaO2>50% but provides reduced accuracy under more severe hypoxic levels.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".