Wound irrigation versus swabbing technique for cleansing noninfected chronic wounds: A systematic review of differences in bleeding, pain, infection, exudate, and necrotic tissue
Bibliographic record
Abstract
PURPOSE: To systematically summarize and review the existing literature to determine the difference between wound cleansing techniques, irrigation and swabbing, in relation to bleeding, pain, infection, necrotic tissue and exudate in non-infected chronic wounds including pressure injuries, venous and arterial leg ulcers and diabetic foot ulcers. METHODS: A systematic search of the electronic databases Ovid Medline, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and EMBASE was performed to identify all relevant literature in English. The search also included systematic reviews as a method to obtain additional potential citations by manually searching the reference lists. Included studies were assessed for methodological quality using the Cochrane Risk of Bias Tool. RESULTS: One study met eligibility criteria. Two hundred fifty six patients with wounds healing via secondary intention (n = 256) were included. Wound cleansing via swabbing technique was associated with increased perception of pain and increased rates of infection when compared to the irrigation group (93.4% versus 84.2% p = 0.02 and 5.2% versus 3.3% p = 0.44, respectively). Only a small proportion of this sample met the inclusion criteria, so the results are not considered externally valid. CONCLUSION: Wound cleansing remains a controversial topic. Despite calls for further research, there continues to remain a large gap in evidence to guide practice. Irrigation continues to replace swabbing in the management of chronic wounds, although evidence of improved outcomes is virtually nonexistent. Although the one study identified was of sound methodological quality, chronic wounds accounted for only a small percentage of the sample. Therefore, results are not generalizable to those with chronic wounds. Further research is needed to determine the effectiveness of basic wound cleansing techniques before considering more costly products.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".