Measurement of Cerebral Oxidative Metabolism with Near-Infrared Spectroscopy: A Validation Study
Bibliographic record
Abstract
Predicting the onset of secondary energy failure after a hypoxic-ischemic insult in newborns is critical for providing effective treatment. Measuring reductions in the cerebral metabolic rate of oxygen (CMRO(2)) may be one method for early detection, as hypoxia-ischemia is believed to impair oxidative metabolism. We have developed a near-infrared spectroscopy (NIRS) technique based on the Fick Principle for measuring CMRO(2). This technique combines cerebral blood flow (CBF) measurements obtained using the tracer indocyanine green with measurements of the cerebral deoxy-hemoglobin (Hb) concentration. In this study, NIRS measurements of CMRO(2) were compared with CMRO(2) determined from the product of CBF and the cerebral arteriovenous difference in oxygen measured from blood samples. The blood samples were collected from a peripheral artery and the sagittal sinus. Eight piglets were subjected to five cerebral metabolic states created by varying the plane of anesthesia. No significant difference was found between CMRO(2) measurements obtained with the two techniques at any anesthetic level (P>0.5). Furthermore, there was a strong correlation when concomitant CMRO(2) values from the two techniques were compared (R(2)=0.88, P<0.001). This work showed that CMRO(2) can be determined accurately by combining NIRS measurements of CBF and Hb. Since NIRS is safe and measurements can be obtained at the bedside, it is believed that this technique could assist in the early diagnosis of cerebral energy dysfunction after hypoxia-ischemia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".