O1-S03.04 Core groups, antimicrobial resistance and rebound in gonorrhoea
Bibliographic record
Abstract
Background Neisseria gonorrhoeae(NG) is a major cause of sexually transmitted infection worldwide. Surveillance data from North America suggest that incidence has increased in recent years, after initially falling in the face of intensified control efforts, as antimicrobial resistance in NG has increased. We evaluated the likely mechanisms behind such rebound using simple compartmental models, and explored the implications of such rebound for disease control practice. Methods We evaluated the impact of risk-focussed treatment strategies on long-term gonorrhoea trends using risk-structured susceptible-infectious-susceptible" (SIS) compartmental models that included and excluded the possibility of antibiotic resistance in gonorrhoea transmission and control. We also examined optimal treatment strategies to minimise gonorrhoea rates when more than one antibiotic is available. Results Model projections, consistent with previous work, showed that when antibiotic resistance is not possible, strategies that focus on treatment of highest risk individuals (the so-called “core group”), result in collapse of gonorrhoea transmission see Abstract O1-S03.04 figure 1. In contrast, in the presence of antimicrobial resistance, a focus on the core group causes rebound in incidence, with maximal dissemination of antibiotic resistance. When two antibiotics are available for treatment, we found that random assignment of treatment was most effective at delaying rebound in overall rates in the population, while the current strategy, which is to switch first-line treatment when a threshold level of resistance is reached, produced the quickest rebound. Abstract O1-S03.04 Figure 1 Prevalence over time with risk group-focused treatment strategies. Conclusions While previous models have shown that the targeted treatment of core-group individuals is the most effective at lowering rates of gonorrhoea, our model suggests that core group-focused treatment strategies efficiently disseminate antimicrobial resistant strains of NG, with rebound in gonorrhoea rates. This paradox poses a great dilemma to the control and prevention of gonorrhoea, especially when development of new antibiotic classes has lagged in recent years and vaccine development for gonorrhoea still faces many challenges. Our study highlighted the need for focus on non-antimicrobial strategies for the prevention and control of gonorrhoea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".