Fluoroquinolone Susceptibility among Mycobacterium tuberculosis Isolates from the United States and Canada
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in the possible role of new fluoroquinolone antibiotics for treatment of tuberculosis, but widespread use of fluoroquinolones for treatment of other bacterial infections may select for resistant strains of Mycobacterium tuberculosis. METHODS: We evaluated fluoroquinolone susceptibility using the proportion method (critical ciprofloxacin concentration for susceptibility testing, 2.0 mu g/mL) in isolates obtained from patients enrolled in Tuberculosis Trial Consortium clinical trials during the period of 1995-2001 and in a referral sample of isolates sent to the Centers for Disease Control and Prevention (Atlanta, GA) during the period of 1996-2000 for additional testing, often because of drug resistance. RESULTS: Of the 1373 isolates from the clinical trials, 1324 (96%) were susceptible to isoniazid and rifampin; 2 (0.15%) of these isolates were also resistant to ciprofloxacin. Of the 1852 isolates from the referral sample, 603 (32.6%) were resistant to isoniazid and rifampin (i.e., multidrug resistant), 849 (45.7%) were resistant to >or=1 first-line drug but were not resistant to both isoniazid and rifampin, and 400 (21.6%) were susceptible to all first-line agents. Ciprofloxacin resistance was found in 33 (1.8%) of the referral-sample isolates. Most ciprofloxacin-resistant isolates (25 [75.8%]) were resistant to isoniazid and rifampin. CONCLUSIONS: Despite widespread use of fluoroquinolones for treatment of common bacterial infections, resistance among clinical isolates of M. tuberculosis in the United States and Canada remains rare, occurring primarily among multidrug-resistant strains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".