Pseudomonas aeruginosa wound infections: a critical appraisal of topical antiseptics
Bibliographic record
Abstract
Background: Pseudomonas aeruginosa infection is a serious complication of burns and wounds with a high degree of morbidity and mortality. This paper reviews the literature on the use of topical antiseptics for the treatment of P. aeruginosa wound infections. Methods: Embase and PubMed were searched, yielding 119 results that were reviewed. Inclusion criteria were all papers that assessed patients with confirmed P. aeruginosa wound infection treated with topical antiseptics and reported the eradication of the pathogen. Results: Three papers met the inclusion criteria, with a total sample size of 33. All of the studies analyzed acetic acid for the treatment of P. aeruginosa wound infection. The pooled data demonstrated that 84.8% of confirmed P. aeruginosa wound infections were effectively treated with acetic acid. Conclusions: P. aeruginosa remains a serious infection complicating burn and wound healing. Treatment commonly includes topical antiseptics; however, there is a paucity of clinical based trials to support this. Three small trials demonstrated some evidence for the use of acetic acid as a topical treatment. More evidence is required to demonstrate the efficacy of topical antiseptics as well as recommend specific types of topical treatment for this pathogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".