Negative-pressure wound therapy for management of chronic neuropathic noninfected diabetic foot ulcerations – short-term efficacy and long-term outcomes
Bibliographic record
Abstract
PURPOSE: Negative pressure wound therapy (NPWT) is an adjunct method used in the treatment of diabetic foot ulceration (DFU). Real world data on its effectiveness and safety is scarce. In this prospective observational study, we assessed the short-term efficacy, safety, and long-term outcomes of NPWT in patients with type 2 diabetes (T2DM) and neuropathic, noninfected DFUs. METHODS: ), 75 patients with DFUs treated in an outpatient clinic were assigned to NPWT (n = 53) or standard therapy (n = 22). Wound area reduction was evaluated after 8 ± 1 days. Long-term outcomes assessed included complete ulceration closure and recurrence rate. RESULTS: , -18.0%, p = 0.0038). No serious adverse events related to NPWT were noted. Within 1 year, 55.1% (27/49) of DFUs were closed in the NPWT group and 73.7% (14/19) in the comparator group (p = 0.15). In the logistic regression, wound duration and smaller initial area, but not treatment mode, were associated with closure. One-year follow-up after DFU resolution revealed an ~30.0% recurrence rate in both groups (p = 0.88). CONCLUSIONS: NPWT is a safe treatment for neuropathic, nonischemic, and noninfected DFU in patients with T2DM, although this observational study did not prove its effectiveness over standard therapy. Additionally, we report a high rate of both closure and recurrence of ulcers, the latter irrespective of initial ulcer area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".