Hyperbaric Oxygen Therapy: A New Treatment for Chronic Pain?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Hyperbaric oxygen therapy (HBOT) is a treatment providing 100% oxygen at a pressure greater than that at sea level. HBOT is becoming increasingly recognized as a potential treatment modality for a broad range of ailments, including chronic pain. In this narrative review, we discuss the current understanding of pathophysiology of nociceptive, inflammatory and neuropathic pain, and the body of animal studies addressing mechanisms by which HBOT may ameliorate these different types of pain. Finally, we review clinical studies suggesting that HBOT may be useful in treating chronic pain syndromes, including chronic headache, fibromyalgia, complex regional pain syndrome, and trigeminal neuralgia. DATABASE AND DATA TREATMENT: A comprehensive search through MEDLINE, EMBASE, Scopus, and Web of Science for studies relating to HBOT and pain was performed using the following keywords: hyperbaric oxygen therapy or hyperbaric oxygen treatment (HBOT), nociceptive pain, inflammatory pain, neuropathic pain, HBOT AND pain, HBOT AND headache, HBOT AND fibromyalgia, HBOT AND complex regional pain syndrome, and HBOT AND trigeminal neuralgia. RESULTS: Twenty-five studies examining the role of HBOT in animal models of pain and human clinical trials were found and reviewed for this narrative review. CONCLUSIONS: HBOT has been shown to reduce pain using animal models. Early clinical research indicates HBOT may also be useful in modulating human pain; however, further studies are required to determine whether HBOT is a safe and efficacious treatment modality for chronic pain conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".