Bibliographic record
Abstract
Antibiotic resistance continues to hamper antimicrobial chemotherapy of infectious disease, and while biocide resistance outside of the laboratory is as yet unrealized, in vitro and in vivo episodes of reduced biocide susceptibility are not uncommon. Efflux mechanisms, both drug-specific and multidrug, are important determinants of intrinsic and/or acquired resistance to these antimicrobials in important human pathogens. Multidrug efflux mechanisms are generally chromosome-encoded, with their expression typically resultant from mutations in regulatory genes, while drug-specific efflux mechanisms are encoded by mobile genetic elements whose acquisition is sufficient for resistance. While it has been suggested that drug-specific efflux systems originated from efflux determinants of self-protection in antibiotic-producing Actinomycetes, chromosomal multidrug efflux determinants, at least in Gram-negative bacteria, are appreciated as having an intended housekeeping function unrelated to drug export and resistance. Thus, it will be important to elucidate the intended natural function of these efflux mechanisms in order, for example, to anticipate environmental conditions or circumstances that might promote their expression and, so, compromise antimicrobial chemotherapy. Given the clinical significance of antimicrobial exporters, it is clear that efflux must be considered in formulating strategies for treatment of drug-resistant infections, both in the development of new agents, for example, less impacted by efflux or in targeting efflux directly with efflux inhibitors.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".