Relationship between cerebral near-infrared spectroscopy signals and intracranial pressure: a systematic scoping review of the human and animal literature
Bibliographic record
Abstract
Abstract Objective. Monitoring of intracranial pressure (ICP) in a clinical environment is critically important to the stability of patients with various acute neurological illnesses and injury including ischemic stroke, hemorrhagic stroke, brain tumor, and traumatic brain injury. This is because changes in ICP can cause significant stress on the brain and surrounding tissue through complications such as cerebral ischemia or hemorrhage in the surrounding area. Most ICP measurement techniques are invasive, expensive, and have poor spatial resolution. There has been some preliminary evidence to suggest that regional oxygen saturation (rSO 2 ) measured non-invasively by near-infrared spectroscopy (NIRS) has a statistical link to invasively obtained ICP. Given the limited exploration of this potential link, this scoping review (ScR) aims to investigate the current body of literature exploring the association between cerebral NIRS measurements and ICP. Approach. A comprehensive investigation was conducted across six major databases, with accordance to the preferred reporting items for systematic reviews and meta-analyzes guidelines, in order to evaluate the primary question of: What is the relationship between NIRS-derived cerebral signals and ICP?. Main results . The search process identified 3791 distinct articles. After screening based on the predefined criteria, 10 studies were deemed eligible for inclusion. An additional two studies were identified by screening the citation lists of the included studies. Overall, the collection of articles selected for this systematic ScR indicates a potential positive correlation between some cerebral NIRS variables and ICP; however, significant discrepancies and significant limitations exist in the literature. Significance. This review identifies a significant knowledge gap in the current understanding of how non-invasive NIRS metrics relate to ICP and highlights the importance of conducting additional experimentation in the field.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".