An Old Drug for a New Application: Potential Benefits of Sildenafil in Wound Healing
Bibliographic record
Abstract
PURPOSE: Several studies have evaluated the effects of sildenafil on the tissue repair and wound healing. In the present review, the impact of sildenafil on the wound healing in all available clinical and non-clinical (experimental) studies has been discussed. METHODS: A literature search was performed using PubMed, Scopus, Medline, Embase, Cochrane central register of controlled trials and Cochrane database systematic reviews. Related articles indexed in Google Scholar were also included. Key words used as search terms were 'phosphodiesterase inhibitor', 'sildenafil', 'skin', 'cutaneous', 'skin lesion', 'skin damage', 'wound', and 'wound healing'. No time limitation was considered in this review. RESULTS: A total of 15 animal studies, 7 case reports, and 2 small clinical studies have reported the effects of sildenafil on the wound healing. The effects included skin flaps and grafts, anastomosis, systemic sclerosis and Raynaud's disease. CONCLUSIONS: The available data support the beneficial effects of sildenafil in improvement of tissue healing in various conditions. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".