Commercial NIRS May Not Detect Hemispheric Regional Disparity in Continuously Measured COx/COx-a: An Exploratory Healthy and Cranial Trauma Time-Series Analysis
Bibliographic record
Abstract
Continuous metrics of cerebral autoregulation (CA) assessment have been developed using various multimodal cerebral physiological monitoring devices. However, CA regional disparity remains unclear in states of health and disease. Leveraging existing archived data sources, we preliminarily evaluated regional hemispheric disparity in CA using the near infrared spectroscopy (NIRS)-derived cerebral oximetry index (COx/COx-a). Along with bilateral NIRS, regional cerebral oxygen saturation, arterial blood pressure, cerebral perfusion pressure, and bilateral COx/COx-a were derived using three different temporal resolutions-10 s, 1 min, and 5 min-based on non-overlapping mean values. The regional disparity between hemispheres was evaluated based on median and median absolute deviation. Further, patient-level autoregressive integrative moving average models were calculated for each signal stream and used to generate personalized vector autoregressive models. Multi-variate cerebral physiologic relationships between hemispheres were assessed via impulse response functions and Granger causality analyses. Data from 102 healthy control volunteers, 27 spinal surgery patients, and 95 TBI patients (varying in frontal lobe pathology impacting the optode path; 64 without bifrontal lobe pathology, 15 without left frontal lobe pathology, 11 without right frontal lobe pathology, and 5 with bifrontal lobe pathology) were retrospectively analyzed. For subjects with or without cranial pathology, no difference in COx/COx-a was found between hemispheres regardless of the analytic method. In TBI patients without pathology underneath the NIRS sensor, distant parenchymal injury does not seem to have an effect on the CA of uninjured frontal lobes. Further work is required to characterize regional disparities with multi-channel CA measurements in healthy and disease states.
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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) | 0.000 | 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) | 0.001 | 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".