Sensitivity of a full-head coverage high-density time-resolved NIRS device to carotid compressions
Bibliographic record
Abstract
Neuromonitoring during cardiac surgery helps prevent brain injury by detecting evidence of cerebral ischemia. Current neuromonitoring devices, such as cerebral oximeters, generally monitor one brain region, which prevents the detection of blood flow impairment in other vascular territories. A potential solution is to use a device with full-head coverage such as the newly developed high-density time-resolved NIRS system, Kernel Flow. This work aimed to assess Kernel Flow’s sensitivity to regional cerebral oxygenation changes using momentary carotid compression (CC), a paradigm that causes substantial decreases in cerebral blood flow and oxyhemoglobin (HbO) throughout the ipsilateral hemisphere. Five healthy volunteers were imaged using a Kernel Flow headset during a 30-s CC. To assess the sensitivity of the device to regional changes, the number of good quality channels was compared between four brain regions: frontal, somatosensory, temporal, and occipital. HbO and deoxyhemoglobin (HbR) time series in the ipsilateral and contralateral hemispheres were analyzed. Overall, the frontal region had the largest amount of good-quality channels, and the ipsilateral regions showed the expected HbO decrease during CC. All contralateral regions showed minimal changes during CC, as expected. Overall, the Flow device showed good sensitivity to reduced cerebral blood flow; however, its use as a neuromonitor during cardiac surgery could be challenged by signal degradation due to hair, although this may be less of an issue with cardiac patients considering that most are older and have less hair.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".