Comparison of time-resolved and continuous-wave near-infrared techniques for measuring cerebral blood flow in piglets
Bibliographic record
Abstract
A primary focus of neurointensive care is monitoring the injured brain to detect harmful events that can impair cerebral blood flow (CBF), resulting in further injury. Since current noninvasive methods used in the clinic can only assess blood flow indirectly, the goal of this research is to develop an optical technique for measuring absolute CBF. A time-resolved near-infrared (TR-NIR) apparatus is built and CBF is determined by a bolus-tracking method using indocyanine green as an intravascular flow tracer. As a first step in the validation of this technique, CBF is measured in newborn piglets to avoid signal contamination from extracerebral tissue. Measurements are acquired under three conditions: normocapnia, hypercapnia, and following carotid occlusion. For comparison, CBF is concurrently measured by a previously developed continuous-wave NIR method. A strong correlation between CBF measurements from the two techniques is revealed with a slope of 0.79±0.06, an intercept of -2.2±2.5 ml∕100 g∕min, and an R2 of 0.810±0.088. Results demonstrate that TR-NIR can measure CBF with reasonable accuracy and is sensitive to flow changes. The discrepancy between the two methods at higher CBF could be caused by differences in depth sensitivities between continuous-wave and time-resolved measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".