The efficacy of topical oxygen therapy for wound healing: A meta‐analysis of randomized controlled trials and observational studies
Bibliographic record
Abstract
In preclinical studies, topical oxygen treatment (TOT) was shown to enhance wound healing by applying supplemental oxygen topically to the surface of a moist wound at normobaric conditions. The objective of this systematic review and meta-analysis is to provide a thorough evaluation of published RCTs and observational studies that compare supplemental TOT with standard wound care. A total of 1077 studies were obtained from a variety of databases, including PubMed, ScienceDirect, Web of Science, ProQuest, Scopus, ClinicalTrials.gov, EU Clinical Trial Registers, and Preprints.org. The Jadad scale was employed to assess the reliability of RCT studies, while the Newcastle-Ottawa Scale (NOS) was employed to assess the quality of observational studies. Seven RCT studies (n = 692) and two controlled observational studies (n = 111) were analysed. The rate of healed wounds was 25.8% in the control group and 43.25% in the adjuvant TOT group, which shows the use of TOT significantly increased the number of healed wounds (RR = 1.77; 95% CI 1.18-2.64; p = 0.005). A significant decrease in the percentage of wound area was found in the TOT group in RCT studies (mean difference = 15.64; 95% CI 5.22-26.06; p = 0.003). In observational studies, the rate of healed wounds was 37.5% in the standard care group and 80.95% in the adjuvant TOT group, which shows a significant increase in the number of healed wounds in the adjuvant TOT group (RR = 2.15; 95% CI 1.46-3.15; p < 0.00001). Topical oxygen therapy is considered a great adjuvant therapy for chronic wound healing, particularly wounds with vascular compromise such as diabetic ulcers and pressure ulcers. Further studies on this topic are still needed as there are a lot of potential uses for this technology in various types of wounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.057 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".