Does Microbicide Use in Consumer Products Promote Antimicrobial Resistance? A Critical Review and Recommendations for a Cohesive Approach to Risk Assessment
Bibliographic record
Abstract
The increasing use of microbicides in consumer products is raising concerns related to enhanced microbicide resistance in bacteria and potential cross resistance to antibiotics. The recently published documents on this topic from the European Commission have spawned much interest to better understand the true extent of the putative links for the benefit of the manufacturers, regulators, and consumers alike. This white paper is based on a 2-day workshop (SEAC-Unilever, Bedford, United Kingdom; June 2012) in the fields of microbicide usage and resistance. It identifies gaps in our knowledge and also makes specific recommendations for harmonization of key terms and refinement/standardization of methods for testing microbicide resistance to better assess the impact and possible links with cross resistance to antibiotics. It also calls for a better cohesion in research in this field. Such information is crucial to developing any risk assessment framework on microbicide use notably in consumer products. The article also identifies key research questions where there are inadequate data, which, if addressed, could promote improved knowledge and understanding to assess any related risks for consumer and environmental safety.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".