Does UV Light as an Adjunct to Conventional Treatment Improve Healing and Reduce Infection in Wounds? A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of UV light on wound healing and infection in patients with skin ulcers or surgical incisions. Outcomes of interest included healing time, wound size and appearance, bacterial burden, and infection. DATA SOURCES: Ovid MEDLINE, Embase, Cochrane, PubMed, CINAHL, and Web of Science. STUDY SELECTION: Comparative and noncomparative clinical studies were considered, including observational cohort, retrospective, and randomized controlled studies. They addressed the research question: "Does the use of UV light as an adjunct to conventional treatment help improve healing and reduce infection in wounds?" Selection criteria included any English language study in adults who used UV light to improve wound healing and prevent or treat wound infection. DATA EXTRACTION: Authors extracted information pertaining to patient demographics, treatment protocols, and the following wound outcomes: appearance, healing time, infection, and bacterial burden. DATA SYNTHESIS: The search yielded 30,986 articles, and screening resulted in 11 studies that underwent final analysis. Of these (N = 27,833), seven (64%) demonstrated an improvement in healing outcomes with adjunctive UV therapy, and the results of four (36%) achieved statistical significance. CONCLUSIONS: There is limited research on the utility of adjunctive UV therapy to improve wound healing outcomes in humans. The majority of literature included in this review supported improved wound healing outcomes with adjuvant UV therapy. Future well-designed randomized controlled trials will be essential in further determining the benefit and utility of UV therapy in wound healing.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".