Efficacy and safety of hyperbaric oxygen treatment in SARS-COV-2 (COVID-19) pneumonia: a systematic review
Bibliographic record
Abstract
INTRODUCTION: The need for intubation and mechanical ventilation among COVID-19 patients is associated with high mortality rates and places a substantial burden on the healthcare system. There is a strong pathophysiological rationale suggesting that hyperbaric oxygen treatment (HBOT), a low-risk and non-invasive treatment, may be beneficial for COVID-19 patients. This systematic review aimed to explore the potential effectiveness and safety of HBOT for treating patients with COVID-19. METHODS: Medline, Embase, Scopus, and Google Scholar were searched from December 2019 to February 2021, without language restrictions. The grey literature was searched via an internet search engine and targeted website and database searches. Reference lists of included studies were searched. Independent reviewers assessed studies for eligibility and extracted data, with disagreements resolved by consensus or a third reviewer. Risk of bias was assessed using the Newcastle Ottawa Scale. Data were summarised descriptively. RESULTS: Six publications (one cohort study, five case reports/series) met the inclusion criteria with a total of 37 hypoxaemic COVID-19 patients treated with HBOT. Of these 37 patients, the need for intubation and mechanical ventilation and in-hospital survival were assessed for 26 patients across three studies. Of these 26 patients, intubation and mechanical ventilation were not required for 24, and 23 patients survived. No serious adverse events of HBOT in COVID-19 patients were reported. No randomised trials have been published. CONCLUSIONS: Limited and weak evidence from non-randomised studies including one propensity-matched cohort study suggests HBOT is safe and may be a promising intervention to optimise treatment and outcomes in hypoxaemic COVID-19 patients. Randomised controlled studies are urgently needed.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".