Decreased Azithromycin Susceptibility of<i>Neisseria gonorrhoeae</i>Isolates in Patients Recently Treated with Azithromycin
Bibliographic record
Abstract
Background: Increasing azithromycin usage and resistance in Neisseria gonorrhoeae threatens current dual treatment. Because antimicrobial exposure influences resistance, we analyzed the association between azithromycin exposure and decreased susceptibility of N. gonorrhoeae. Methods: We included N. gonorrhoeae isolates of patients who visited the Amsterdam STI Clinic between 1999 and 2013 (t0), with another clinic visit in the previous 60 days (t-1). Exposure was defined as the prescription of azithromycin at t-1. Using multivariable linear regression, we assessed the association between exposure and azithromycin minimum inhibitory concentration (MIC). Whole genome sequencing (WGS) was performed to produce a phylogeny and identify multilocus sequence types (MLST), N. gonorrhoeae multiantigen sequence types (NG-MAST), and molecular markers of azithromycin resistance. Results: We included 323 isolates; 212 were unexposed to azithromycin, 14 were exposed ≤30 days, and 97 were exposed between 31 and 60 days before isolation. Mean azithromycin MIC was 0.28 mg/L (range, <0.016-24 mg/L). Linear regression adjusted for age, ethnicity, infection site, and calendar year showed a significant association between azithromycin exposure ≤30 days and MIC (β, 1.00; 95% confidence interval, 0.44-1.56; P = .002). WGS was performed on 31 isolates: 14 unexposed, 14 exposed to azithromycin ≤30 days before isolation, and 3 t-1 isolates. Exposure to azithromycin was significantly associated with A39T or G45D mtrR mutations (P = .046) but not with MLST or NG-MAST types. Conclusions: The results suggest that frequent azithromycin use in populations at high risk of contracting N. gonorrhoeae induces an increase in MIC and may result in resistance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".