Hyperbaric oxygen therapy for the treatment of long COVID: early evaluation of a highly promising intervention
Bibliographic record
Abstract
BACKGROUND: Long COVID is a common occurrence following COVID-19 infection. The most common symptom reported is fatigue. Limited interventional treatment options exist. We report the first evaluation of hyperbaric oxygen therapy (HBOT) for long COVID treatment. METHODS: A total of 10 consecutive patients received 10 sessions of HBOT to 2.4 atmospheres over 12 days. Each treatment session lasted 105 minutes, consisting of three 30-minute exposures to 100% oxygen, interspersed with 5-minute air breaks. Validated fatigue and cognitive scoring assessments were performed at day 1 and 10. Statistical analysis was with Wilcoxon signed-rank testing reported alongside effect sizes. RESULTS: HBOT yielded a statistically significant improvement in the Chalder fatigue scale (p=0.0059; d=1.75 (very large)), global cognition (p=0.0137; d=-1.07 (large)), executive function (p=0.0039; d=-1.06 (large)), attention (p=0.0020; d=-1.2 (very large)), information processing (p=0.0059; d=-1.25 (very large)) and verbal function (p=0.0098; d=-0.92 (large)). CONCLUSION: Long COVID-related fatigue can be debilitating, and may affect young people who were previously in economic employment. The results presented here suggest potential benefits of HBOT, with statistically significant results following 10 sessions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".