<i>In vivo</i>assessment of odour retention in an antimicrobial silver chloride-treated polyester textile
Bibliographic record
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.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".