Impact of Extracranial Contamination on Regional Cerebral Oxygen Saturation
Bibliographic record
Abstract
BACKGROUND: Cerebral oximetry is a noninvasive technology using near-infrared spectroscopy (NIRS) to estimate regional cerebral oxygen saturation. Although NIRS cerebral oximetry is being increasingly used in many clinical settings, interdevice technologic differences suggest potential variation in the ability to accurately acquire brain oxygenation signals. The primary objective of this study was to determine if NIRS-derived regional cerebral oxygen saturation measurements accurately account for oxygen saturation contamination from extracranial tissue. METHODS: Twelve healthy volunteers had each of three NIRS devices (FORE-SIGHT [CAS Medical Systems Inc; Brandford, CT], INVOS 5100C-PB [Covidien; Boulder, CO], and EQUANOX Classic 7600 [Nonin Medical Inc; Plymouth, MN]) randomly applied to the forehead. After this, a circumferential pneumatic head cuff was positioned such that when inflated, hypoxia-ischemia would be produced in the extracranial scalp tissue beneath the NIRS cerebral oximeters. Comparisons among the three devices were made of the NIRS measurements before and following hypoxia-ischemia produced in the scalp tissue with inflation of the head cuff. RESULTS: The induction of extracranial hypoxia-ischemia resulted in a significant reduction in regional cerebral oxygen saturation measurements in all three NIRS devices studied. At 5 min postinflation of the pneumatic head cuff, the INVOS demonstrated a 16.6 ± 9.6% (mean ± SD) decrease from its baseline (P = 0.0001), the FORE-SIGHT an 11.8 ± 5.3% decrease from its baseline (P < 0.0001), and the EQUANOX a 6.8 ± 6.0% reduction from baseline (P = 0.0025). CONCLUSIONS: Extracranial contamination appears to significantly affect NIRS measurements of cerebral oxygen saturation. Although the clinical implications of these apparent inaccuracies require further study, they suggest that the oxygen saturation measurements provided by cerebral oximetry do not solely reflect that of the brain alone.
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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) | 0.000 | 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".