Effect of a surfactant-based gel on patient quality of life
Bibliographic record
Abstract
The characteristic clinical signs of chronic wounds, which remain in a state of prolonged inflammation, include increased production of devitalised tissue and exudate, pain and malodour. The presence of necrotic tissue, slough and copious exudate encourages microbial proliferation, potentially resulting in planktonic and/or biofilm infection. For patients, the consequences can include leakage of exudate, pain and reduced mobility, which can impair their ability to socialise and perform activities of daily living. This can severely reduce their quality of life and wellbeing. Concentrated surfactant-based gels (Plurogel and Plurogel SSD) are used in wound cleansing to help manage devitalised tissue. In vitro studies indicate they can sequester planktonic microbes and biofilm from the wound bed, although there is, limited clinical evidence to support this. A group of health professionals who have used this concentrated surfactant gel, in combination with standard care, in their clinical practice for several years recently met at a closed panel session. Here, they present case studies where topical application of these gels resulted in positive clinical outcomes in previously long-standing recalcitrant wounds. In all cases, the reduction in inflammation and bioburden alleviated symptoms that previously severely impaired health-related quality of life and wellbeing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".