Efficacy and safety of hyperbaric oxygen treatment to treat COVID-19 pneumonia: a living systematic review update
Bibliographic record
Abstract
INTRODUCTION: As the COVID-19 pandemic evolves, new effective treatment options are essential for reducing morbidity and mortality as well as the strain placed on the healthcare system. Since publication of our initial review on hyperbaric oxygen treatment (HBOT) for hypoxaemic COVID-19 patients, interest in HBOT for COVID-19 has grown and additional studies have been published. METHODS: For this living systematic review update the previously published search strategy (excluding Google Scholar) was adopted with an extension from 01 February 2021 to 01 April 2022. Study inclusion criteria, data extraction, risk of bias estimation and dispute resolution methods were repeated. RESULTS: Two new studies enrolling 127 patients were included in this update, taking the total to eight studies with 224 patients. Both new studies were randomised controlled trials, one at moderate and one at high risk of bias. Across these eight studies, 114 patients were treated with HBOT. All reported improved clinical outcomes without observation of any serious adverse events. Meta-analysis remained unjustified given the high heterogeneity between studies and incomplete reporting. CONCLUSIONS: This updated living systematic review provides further evidence on the safety and effectiveness of HBOT to treat acute hypoxaemic COVID-19 patients.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".