Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy
Bibliographic record
Abstract
Preterm infants are highly susceptible to ischemic brain injury; consequently, continuous bedside monitoring to detect ischemia before irreversible damage occurs would improve patient outcome. In addition to monitoring cerebral blood flow (CBF), assessing the cerebral metabolic rate of oxygen (CMRO2) would be beneficial considering that metabolic thresholds can be used to evaluate tissue viability. The purpose of this study was to demonstrate that changes in absolute CMRO2 could be measured by combining diffuse correlation spectroscopy (DCS) with time-resolved near-infrared spectroscopy (TR-NIRS). Absolute CBF was determined using bolus-tracking TR-NIRS to calibrate the DCS measurements. Cerebral venous blood oxygenation (SvO2) was determined by multiwavelength TR-NIRS measurements, the accuracy of which was assessed by directly measuring the oxygenation of sagittal sinus blood. In eight newborn piglets, CMRO2 was manipulated by varying the anesthetics and by injecting sodium cyanide. No significant differences were found between the two sets of SvO2 measurements obtained by TR-NIRS or sagittal sinus blood samples and the corresponding CMRO2 measurements. Bland-Altman analysis showed a mean CMRO2 difference of 0.0268 ± 0.8340 mLO2/100 g/min between the two techniques over a range from 0.3 to 4 mL O2/100 g/min.
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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 | 0.000 | 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".