Antibiotic Selection and Resistance Issues with Fluoroquinolones and Doxycycline Against Bioterrorism Agents
Bibliographic record
Abstract
Bacillus anthracis (anthrax), Yersinia pestis (plague), Francisella tularensis (tularemia), Coxiella burnetti (Q fever), and Brucella sp (brucellosis) are all potential bioterrorism agents. Their known virulence, potential lethality, and ability to develop resistance to known antibiotic treatments make these pathogens particularly dangerous. We reviewed the scientific literature by searching MEDLINE databases and published abstracts from the Interscience Conference on Antimicrobial Agents and Chemotherapy and the Infectious Diseases Society of America from 1989-2005 for studies of each of these biologic agents with the specific aim of examining whether doxycycline or a fluoroquinolone should be stockpiled for mass-scale postexposure prophylaxis. An evidence-based approach was used to determine whether doxycycline or fluoroquinolones were efficacious (both in vitro and in vivo) against these biologic agents and to examine these drugs' respective susceptibility patterns and differences in cost, based on available data. Little published data are available on these pathogens, and much of the data are from studies that used older strains obtained from patient or animal sources in outbreaks decades ago. Doxycycline appears to show comparable minimum inhibitory concentrations to those of the fluoroquinolone class in most clinical and in vitro studies, perhaps with the exception of inhalation plague. Studies also suggest that development of antibiotic resistance is less likely to occur with doxycycline. Doxycycline is several-fold less expensive than most fluoroquinolones and appears to have similar efficacy in most scenarios based on clinical case studies and established Clinical and Laboratory Standards Institute (formerly known as the National Committee for Clinical Laboratory Standards) breakpoints for staphylococci. Therefore, doxycycline should be considered as a first-line antibiotic in the management of bioterrorism agents.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".