Meta-Analysis: Effect of Hyperbaric Oxygen Therapy on Diabetic Foot Ulcer Recuperation
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is a global phenomenon due to the high morbidity and mortality, especially in developing countries such as Indonesia. An ulcer is a wound and is usually found in patients who experience increased blood sugar and can cause peripheral blood vessel disorders, blood vessel disorders will result in peripheral neuropathy or a combination of both, diabetic ulcers are a condition often experienced by diabetes sufferers. Hyperbaric Oxygen Therapy (HBOT) is a therapy that is considered effective in healing diabetic ulcers and has been proven by many studies conducted. This study aims to analyze the effect of HBO therapy on the improvement of diabetic ulcer wounds. Subjects and Method: This was a systematic review and meta-analysis of primary studies. Article searches were carried out based on the PICO model. Population: diabetic ulcer patients. Intervention: Hyperbaric Oxygen Therapy. Comparison: placebo. Outcome: diabetic ulcers condition. Article searches were carried out from Google Scholar, MEDLINE/PubMed, Science Direct, Scopus, Hindawi, BMC, and Spinger Link databases. Keywords used "diabetic foot ulcers" AND "hyperbaric oxygen therapy". The inclusion criteria were full paper, RCT, and reporting risk ratio (RR). Selected studies were analyzed using the RevMan 5.3. Results: 9 RCTs from China, Canada, the Netherlands, Sweden, Egypt, Taiwan, England, and Turkey were selected for meta-analysis. HBO therapy increased diabetic ulcers condition (RR= 1.91; 95% CI= 1.17 to 3.12; p= 0.01). Conclusion: HBO therapy improves diabetic ulcers condition. Keywords: hyperbaric oxygen, diabetic foot ulcer, meta analisis.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".