A Novel Bathing Therapeutic Approach for Diabetic Foot Ulcers
Bibliographic record
Abstract
Ineffective healing and treatment of foot ulcers can lead to an infection and gangrene of the wound area that ultimately results in the loss of the limb. The incidence of foot ulcers is higher in patients with diabetes, peripheral vascular disease and kidney disease. Accordingly, this study was undertaken to assess the ability of foot bathing in CO2-enriched water to heal foot ulcers. The design was a double-blinded, randomized, placebo-controlled study. Patients with at least one foot ulcer were randomized to receive either a treatment with bath therapy at 37 ± 0.5 °C containing either 1000–1200 ppm CO2-enriched tap water (the intervention) or non-carbonated tap water at 37 ± 0.5 °C (the control group). Treatment was conducted three times/week for 15 min per session for up to 16 weeks for a total of 48 treatment sessions. Before and at the end of every treatment month, wound size, wound area oxygenation and the ankle brachial index were measured. In addition, the McGill pain questionnaire was conducted. Blood was also collected at these time points (for a total of five collections) for the measurement of different biomarkers. While no significant differences (p < 0.05) in the group/time interaction effect were observed, a clear separation within the wound area reduction/wound area/oxygenated Hb outcomes was seen between placebo (control) and treatment (CO2) group. This pilot study is suggestive that bathing in CO2-enriched water may accelerate the healing of foot ulcers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".