Near Infrared Spectroscopy for Poor Grade Aneurysmal Subarachnoid Hemorrhage—A Concise Review
Bibliographic record
Abstract
Delayed cerebral ischemia (DCI) disproportionately affects poor grade aneurysmal subarachnoid hemorrhage (aSAH) patients. An unreliable neurological exam and the lack of appropriate monitoring leads to unrecognized DCI, which in turn is associated with severe long-term deficits and higher mortality. Near Infrared Spectroscopy (NIRS) offers simple, continuous, real time, non-invasive cerebral monitoring. It provides regional cerebral oxygen saturation (c-rSO2), which reflects the balance between cerebral oxygen consumption and supply. Reports have demonstrated a good correlation with other cerebral oxygen and blood flow monitoring, and credible cerebrovascular reactivity indices were also derived from NIRS signals. Multiple critical c-rSO2 values have been reported in aSAH patients, based on various thresholds, duration, variation from baseline or cerebrovascular reactivity indices. Some were associated with vasospasm, some with DCI and others with clinical outcomes. However, the poor grade aSAH population has not been specifically studied and no randomized clinical trial has been published. The available literature does not support a specific NIRS-based intervention threshold to guide diagnostic or treatment in aSAH patients. We review herein the fundamental basic concepts behind NIRS technology, relationship of c-rSO2 to other brain monitoring values and their potential clinical interpretation. We follow with a critical evaluation of the use of NIRS in the aSAH population, more specifically its ability to diagnose vasospasm, to predict DCI and its association to outcome. In summary, NIRS might offer significant potential for poor grade aSAH in the future. However, current evidence does not support its use in clinical decision-making, and proper technology evaluation is required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".