Safety of hyperbaric oxygen therapy in non-emergent patients with a history of seizures: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Hyperbaric oxygen therapy (HBOT) is well established as a treatment for various medical conditions. However, it poses a risk of oxygen toxicity, which can cause seizures particularly in individuals with pre-existing seizure disorders. Consequently, seizure disorders are considered a relative contraindication to HBOT. Despite this, the relative risk of HBOT-induced seizures in this patient population remains unclear. This retrospective cohort study aims to evaluate the safety of HBOT among patients with pre-existing seizure disorders. METHODS: After obtaining approval from the Research Ethics Board, we retrospectively reviewed the patient charts of individuals with a history of seizures who were referred to the Rouge Valley Hyperbaric Medical Center and Toronto General Hyperbaric Medicine Unit for HBOT between January 2020 and December 2023. Relevant demographic information, past medical history, and HBOT session treatment protocols, such as the treatment pressure set in absolute atmospheric pressure (ATA) and number of air breaks, were recorded. The collected data was analyzed using descriptive statistics. RESULTS: A total of 43 patients were referred to HBOT during the study period, and 21 patients did not proceed with the treatments. In total, 634 HBOT sessions were administered to 22 patients in monoplace chambers with five-minute air breaks, and one patient experienced a seizure event. Each patient completed an average of 29 (range 3-60) sessions lasting 90-120 minutes at 1.8 ATA (n = 3), 2.0 ATA (n = 18), or 2.4 ATA (n = 1). Fifteen patients were on oral antiseizure medications during the HBOT course. The overall incidence of seizures was one in 634 treatments. CONCLUSION: While patients with a history of seizures may develop seizure activity during HBOT, the majority can safely undergo treatment when predetermined protocols are followed. With careful management and adherence to established protocols, HBOT can be a viable treatment option for those with seizure histories.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".