Continuous monitoring of absolute cerebral blood flow by near-infrared spectroscopy during global and focal temporary vessel occlusion
Bibliographic record
Abstract
Treatment of intracranial aneurysms by surgical clipping carries a risk of intraoperative ischemia, caused mainly by prolonged temporary occlusion of cerebral arteries. The objective of this study was to develop a near-infrared spectroscopy (NIRS) technique for continuous monitoring of cerebral blood flow (CBF) during surgery. With this approach, cerebral hemodynamics prior to clipping are measured by a bolus-tracking method that uses indocyanine green as an intravascular contrast agent. The baseline hemodynamic measurements are then used to convert the continuous Hb difference (HbD) signal (HbD = oxyhemoglobin - deoxyhemoglobin) acquired during vessel occlusion to units of CBF. To validate the approach, HbD signal changes, along with the corresponding CBF changes, were measured in pigs following occlusion of the common carotid arteries or a middle cerebral artery. For both occlusion models, the predicted CBF change derived from the HbD signal strongly correlated with the measured change in CBF. Linear regression of the predicted and measured CBF changes resulted in a slope of 0.962 (R(2) = 0.909) following carotid occlusion and 0.939 (R(2) = 0.907) following middle cerebral artery occlusion. These results suggest that calibrating the HbD signal by baseline hemodynamic measurements provides a clinically feasible method of monitoring CBF changes during neurosurgery.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".