HYPERBARIC OXYGEN THERAPY FOR DIABETIC ULCERS: SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
OBJECTIVES: Approximately 10-15 percent of individuals with diabetes mellitus develop foot ulcers, which precede 85 percent of amputations. Increased oxygen, through the use of hyperbaric oxygen therapy (HBOT), has been suggested to encourage ulcer healing thus reducing the risk of amputation. The objective of this systematic review is to evaluate the efficacy of systemic HBOT for nonhealing ulcers of the lower limb in diabetes patients. METHODS: A systematic search, using controlled and keyword terms focusing on "HBOT" and "lower limb diabetic ulcers," was conducted. Databases searched included Medline, EMBASE, CINAHL, PubMed, Wiley's Cochrane Library, and Biosis. Randomized controlled trials (RCTs) and observational studies were included. Pooled estimates of outcomes were determined when appropriate. RESULTS: Of the 654 citations identified, 157 articles underwent full-text review. Data were abstracted from twelve publications (six RCTs and six comparative observational studies). Pooled analysis of the RCT and observational data showed that treatment with HBOT reduced the risk of major amputation by 60 percent (p = .29) and 61 percent (p = .003) compared with standard wound care, respectively. The RCT data revealed that the relative risk of having an unhealed wound following HBOT was 0.54 (p = .10) and 0.24 (p < .0001) based on observational data. CONCLUSIONS: Due to the limited RCT evidence, it is not possible to conclusively establish the benefits and harms of treating diabetic lower limb ulcers with HBOT. No significant effects on amputation rates were found in the RCT evidence and in the high quality studies, no difference was found.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".