Effectiveness of Relaxation Therapy for Wound Healing in Patients with Diabetic Foot Ulcers: A Systematic Review of Randomized Control Trials
Bibliographic record
Abstract
Abstract Diabetic foot ulcers (DFU) is a serious complication of diabetes that leads to open sores found on the lower extremity, ultimately decreasing the quality of life. The purpose of this study is to systematically review randomized control trials (RCTs) to evaluate the effectiveness of relaxation therapy on healing wounds in patients with diabetic foot ulcers. The search was conducted across the databases of PubMed, Embase (Ovid), Cochrane Library, Google Scholar, and Web of Science. Inclusion criteria consisted of RCTs that compared relaxation therapy to standard care or other psychological/psychosocial intervention. The primary outcomes looked at were DFU healing, DFU extent, and perceived stress scale. The study included 3 RCTs that enrolled 75 patients with diabetic foot ulcers. It was found that within-intervention, relaxation therapy led to improved DFU healing, DFU extent, and participants were less stressed. Despite this, no evidence was found that suggested that relaxation therapy significantly improves outcomes compared to standard care or neutral imagery. Overall, it cannot be concluded that relaxation therapy has an effect on outcomes as there isn’t enough RCTs to come to a conclusion. Future research should delve into long-term effects of relaxation therapy on wound healing in patients with DFU, as well as compare it to other implemented therapies, such as cognitive behavioral therapy.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".