Triclosen and Its Alternatives in Antibacterial Soaps
Bibliographic record
Abstract
BACKGROUND: In 2017, the Food and Drug Administration banned the marketing of triclosan and triclocarban in antibacterial soaps, citing inefficacy and concerns of systemic absorption and antibacterial resistance. As a result, there is an anticipated decrease in the number of triclosan-containing products on the market with an associated increase in antibacterial alternatives (eg, benzalkonium chloride, benzethonium chloride, chloroxylenol, chlorhexidine) and cases of allergic and irritant contact dermatitis. OBJECTIVES: The aims of the study were to determine the extent that triclosan and triclocarban are being used in the consumer and medical industries after the Food and Drug Administration marketing ban and to appraise the risk of allergic contact dermatitis to triclosan alternatives. METHODS: The National Drug Code Directory (NDCD), Google, Amazon, Target, Walgreens, Walmart, CVS, and Colorado hospitals were surveyed for antibacterial soap use. Antibiotics surveyed include triclosan, triclocarban, benzalkonium chloride, benzethonium chloride, chloroxylenol, and chlorhexidine. RESULTS: The most common antibiotics reported by the NDCD, consumer sites, and Colorado hospitals were benzalkonium chloride, chloroxylenol, and triclosan, respectively. Triclosan accounted for the second most prevalent antibacterial in the NDCD- and consumer site-surveyed products. CONCLUSIONS: The triclosan marketing ban may instigate increased exposure to triclosan alternatives. The addition of antibacterial products to hand soaps does not improve soap effectiveness and may cause harm by contributing to antibiotic resistance and the development of allergic conditions. Additional studies are needed to elucidate the benefits and harms of antibacterial soaps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".