Impact of Skin Pigmentation on Cerebral Regional Saturation of Oxygen Using Near-Infrared Spectroscopy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Near-infrared spectroscopy (NIRS) is used in critical care settings to measure regional cerebral tissue oxygenation (rS o 2 ). However, the accuracy of such measurements has been questioned in darker-skinned individuals due to the confounding effects of light absorption by melanin. In this systematic review, we aim to synthesize the available evidence on the effect of skin pigmentation on rS o 2 readings. DATA SOURCES: We systematically searched MEDLINE, Cochrane Database of Systematic Reviews, Embase, and Google Scholar from inception to July 1, 2023. STUDY SELECTION: In compliance with our PROSPERO registration (CRD42022347548), we selected articles comparing rS o 2 measurements in adults either between racial groups or at different levels of skin pigmentation. Two independent reviewers conducted full-text reviews of all potentially relevant articles. DATA EXTRACTION: We extracted data on self-reported race or level of skin pigmentation and mean rS o 2 values. DATA SYNTHESIS: Of the 11,495 unique records screened, two studies ( n = 7,549) met our inclusion criteria for systematic review. Sun et al (2015) yielded significantly lower rS o 2 values for African Americans compared with Caucasians, whereas Stannard et al (2021) found little difference between self-reported racial groups. This discrepancy is likely because Stannard et al (2021) used a NIRS platform which specifically purports to control for the effects of melanin. Several other studies that did not meet our inclusion criteria corroborated the notion that skin pigmentation results in lower rS o 2 readings. CONCLUSIONS: Skin pigmentation likely results in attenuated rS o 2 readings. However, the magnitude of this effect may depend on the specific NIRS platform used.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".