Healing Rate and Time to Closure of Venous Leg Ulcers: A Real-World Service Evaluation of Neuromuscular Electrostimulation as an Adjunct to Compression Therapy
Bibliographic record
Abstract
OBJECTIVE: To perform a service evaluation of neuromuscular electrostimulation (NMES) as an adjunct to compression therapy, comparing the rate of wound margin advance and time to closure with a matched retrospective control group. METHODS: Fifteen patients with venous leg ulcers were prescribed NMES for 6 hours per day for 56 days or until wound closure (whichever occurred first), in addition to multilayer compression. Wounds were selected for size, with an inclusion criterion of a maximum of 12 cm 2 . Wound progress was compared with 15 retrospective control patients who were matched for ulcer size and duration. RESULTS: The retrospective group had a healing rate of 0.31 mm per week (95% CI, 29-37 mm/week), whereas the prospective compression plus NMES group had a healing rate of 0.56 mm per week (95% CI, 50-62 mm/week; P = .004). All wounds in both groups healed completely during the service evaluation. Mean time to closure for the retrospective group was 77 days (95% CI, 66-88 days), whereas the NMES group had a mean time to closure of 40 days (95% CI, 37-43 days; P = .005). CONCLUSIONS: Adding NMES of the common peroneal nerve to a care bundle including multicomponent compression resulted in significantly faster wound margin advance and significantly less time to heal in comparison with retrospective matched controls. Future randomized controlled trials or self-controlled studies of this approach would be of great interest to inform clinical practice.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".