Relationship Between Signals from Cerebral near Infrared Spectroscopy Sensor Technology and Objectively Measured Cerebral Blood Volume: A Systematic Scoping Review
Bibliographic record
Abstract
Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles' reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area.
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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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".