Bibliographic record
Abstract
Microbial pathogens continue as major threats to health. Indeed, many ongoing societal changes are enhancing our vulnerability and exposure to several frank and opportunistic pathogens. This, together with rampant antimicrobial resistance and reduced prospects for newer drugs and vaccines, is forcing a higher reliance on microbiocides in infection prevention and control. That this reliance may not be well-founded becomes apparent from a closer look at current ways of testing and registering microbiocides, their label claims as well as human and environmental safety of certain widely used microbicidal chemicals. Many methods to test microbiocides for registration are flawed and/or entail test conditions irrelevant to field use. Pathogens listed on product labels may not be among those amenable to interruption through microbiocide use. The wide variations and discrepancies in existing national/regional regulations for registering microbiocides for sale stifle innovation. This is a critical look at the above-mentioned issues with emphasis on chemicals meant for use on environmental surfaces and medical devices. It highlights better ways to test microbiocides and to attain global harmonization of testing and product registration. It also details the known and potential dangers of microbiocide use and what to consider in choosing such formulations for optimal safety and effectiveness. End users are advised to be more critical and prudent in the selection and application of microbicidal chemicals, manufacturers are encouraged to explore infection control products and technologies that are safer in the workplace and for the environment, and regulators are urged to review and update the requirements and procedures for premarket review of microbiocide efficacy data and label claims. Independent investigations are also urgently needed to document the proportion of nosocomial infections that would be amenable to prevention through chemical disinfection of environmental surfaces.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".