<i>In vivo</i>assessment of odour retention in an antimicrobial silver chloride-treated polyester textile
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The purpose of this study was to determine whether polyester textiles treated with bioactive concentrations of an antimicrobial silver chloride (SC) compound were effective in reducing axillary odour and axillary bacterial populations before and after multiple washes. A polyester knit fabric was treated with two concentrations of a SC formulation (resulting in 30 and 60 ppm of silver) and evaluated at two levels of wash treatments (unwashed and washed 30 times). Treated fabrics were matched with an untreated control fabric and worn against the axillae of male participants (n = 8). A sensory panel evaluated odour intensity using two different methods (paired comparison and line scale method). Overall, results showed that the treated fabrics did not lower odour intensity compared with the untreated fabrics. Bacterial populations extracted from the treated fabrics were also not significantly lower, despite there being evidence of antimicrobial activity in in vitro testing. The paired comparison method was found to be more sensitive in detecting small differences between treated and untreated fabrics. However, the line scale method was deemed to be a more appropriate method for evaluating odour intensity on fabrics because the magnitude of the difference could be assessed. It is recommended that as in vitro efficacy does not necessarily predict in vivo efficacy of an antimicrobial treatment that sensory evaluation and in vivo testing should be conducted when examining the odour reducing properties of an antimicrobial. Keywords: polyestersensory evaluationline scalepaired comparisonodourbacteriawash treatment Acknowledgements We gratefully acknowledge the staff and students from the University of Alberta who participated in this study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it