Could hyperbaric oxygen be an effective therapy option for pathological scars? A systematic review and meta-analysis
Bibliographic record
Abstract
Background Hyperbaric oxygen (HBO) therapy involves breathing pure oxygen or a high oxygen concentration above atmospheric (ATM) pressure in an enclosed chamber. Studies on pathological scars have demonstrated that HBO can inhibit the formation of pathological scars.Objective To evaluate the efficacy of HBO in the treatment of pathological scars via meta-analysis.Methods Searches were run on various databases, including the Cochrane, Embase, PubMed, Web of Science, and CNKI databases. A comparative study was conducted on patients with pathological scars treated with or without HBO. We used RevMan 5.4 software to determine the recurrence rate, treatment satisfaction, and Vancouver Scar Scale(VSS) score in the pathological scar.Results A total of 543 publications were identified; after screening, four were selected for review, including one randomized controlled trial (RCT), one controlled clinical trial (CCT), and two retrospective cohort studies. Meta-analysis results showed that HBO treatment reduced the pathological scar recurrence rate after surgery and radiotherapy (OR = 0.26, 95% CI: 0.13–0.52, p = 0.0001). Patients had higher satisfaction after HBO therapy (OR = 4.45, 95% CI: 1.49–13.30, p = 0.007). The Vancouver scar scale (VSS) score of patients with pathological scars was significantly improved in the HBO group (SMD: −3.82, 95% CI: −6.07to −0.49, p = 0.02).Conclusions HBO treatment decreased the recurrence rate of pathological scars after surgery and radiotherapy, increased patient satisfaction, and reduced the VSS score, thus providing a new way to treat pathological scar hyperplasia. However, evaluation of the longer-term effects of HBO treatment requires further comprehensive studies, including more RCTs.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".