Bibliographic record
Abstract
The fluoroquinolones have excellent in vitro activity against a variety of gastrointestinal pathogens including enteropathogenic Escherichia coli, Salmonella spp., Shigella spp., Yersinia enterocolitica, Aeromonas hydrophila, Plesiomonas shigelloides, and Helicobacter pylori [ 1 - 4 ]. The newer fluoroquinolones (trovafloxacin, gatifloxacin, gemifloxacin) are also active against many obligate anaerobes [ 5 , 6 ]. Coupled with favorable bioavailability after oral administration, and the ability to achieve high concentrations in the intestinal mucosa, hepatobiliary tree, and in feces, it is not surprising that this antibiotic class has been studied extensively for the treatment of gastrointestinal infections. This chapter will review the impact of fluoroquinolones on the fecal flora, and their major clinical applications in gastrointestinal and intraabdominal infections. Particular emphasis is directed to large randomized, double-blind and controlled clinical trials before and since 1995. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.021 |
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".