Abstract 122: Efficacy and Safety of Hyperbaric Oxygen Therapy for Retinal Artery Occlusion: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Retinal artery occlusion (RAO) is a vision‐threatening condition with limited therapeutic options. Hyperbaric oxygen therapy (HBOT) has emerged as a potential treatment to enhance retinal oxygenation and salvage ischemic tissue, though its efficacy and safety remain debated. Methods We followed the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Databases were searched through November 2024 for studies comparing HBOT with control in RAO patients. Risk of bias was assessed using the Newcastle‐Ottawa Scale (NOS). Meta‐analyses evaluated visual acuity (VA), best‐corrected visual acuity (BCVA), and adverse events (AEs). Results Nine studies with 499 patients (286 HBOT, 213 non‐HBOT) met the inclusion criteria. HBOT was associated with improved BCVA (MD: ‐0.63, 95% CI: [‐1.14, ‐0.12], p=0.01) after sensitivity analysis. No significant differences were observed in uncorrected VA or lines of improvement. AEs included seizures (1.47%), ear barotrauma (1.65%), and epistaxis (0.83%) in the HBOT group. Notably, HBOT was associated with lower rates of neovascular glaucoma (7.89% vs. 15.79%) and stroke (4.3% vs. 16.6%) compared to controls. Conclusions HBOT demonstrates potential for improved visual outcomes in RAO patients, particularly BCVA, with a generally favorable safety profile. However, heterogeneity among studies and limited sample sizes highlight the need for robust prospective trials to clarify its role in RAO management. Highlights Hyperbaric oxygen therapy (HBOT) improves best‐corrected visual acuity (BCVA) in retinal artery occlusion (RAO) patients with a favorable safety profile. Patients receiving HBOT show lower rates of glaucoma (7.89% vs. 15.79%) and stroke (4.3% vs. 16.6%) compared to controls. Further large‐scale prospective trials are needed to validate HBOT's role in RAO treatment due to study variability. image image
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".