Electroencephalographic (EEG) Changes Accompanying Normal Breathing of Concentrated Oxygen (Hyperoxic Ventilation) by Healthy Adults: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Divers often increase their fraction of inspired oxygen (FiO 2 ) to decrease their risk of decompression sickness. However, breathing concentrated oxygen can cause hyperoxia, and central nervous system oxygen toxicity (CNS-OT). This study aims to review the literature describing hyperoxic ventilation’s effect on the electroencephalogram (EEG), thus exploring the potential for real-time detection of impending CNS-OT seizure. Methods We searched Medline, Embase, Scopus, and Web of Science for articles that reported EEG measures accompanying hyperoxic ventilation (FiO 2 = 1.0) in healthy participants. We included peer-reviewed journal articles, books, and government reports with no language or date restrictions. Randomised controlled trials and cross-over studies were included; case reports were excluded. We used the Newcastle-Ottawa scale to evaluate evidence quality. Results Our search strategy returned 1025 unique abstracts; we analysed the full text of 40 articles; 22 articles (16 studies) were included for review. Study cohorts were typically small, and comprised of male non-divers. We discovered a variety of EEG analysis methods: studies performed spectral analysis ( n = 12), the analysis of sensory-evoked potentials ( n = 4), connectivity/complexity analysis ( n = 3), source localization ( n = 1), and expert qualitative analyses ( n = 4). Studies of severe exposures (long duration at hyperbaric pressure) typically reported qualitative measures, and studies of mild exposures typically reported quantitative measures. Conclusions There is a need for a large randomised controlled trial (RCT) reporting quantitative measures to better understand hyperoxic ventilation’s effect on EEG, thus enabling the development of real-time monitoring of CNS-OT risk.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".