Core groups, antimicrobial resistance and rebound in gonorrhoea in North America
Bibliographic record
Abstract
BACKGROUND: Genital tract infections caused by Neisseria gonorrhoeae are a major cause of sexually transmitted disease worldwide. Surveillance data suggest that incidence has increased in recent years after initially falling in the face of intensified control efforts. OBJECTIVES: The authors sought to evaluate the potential contribution of antimicrobial resistance to such rebound and to identify optimal treatment strategies in the face of resistance using a mathematical model of gonorrhoea. METHODS: The authors built risk-structured 'susceptible-infectious-susceptible' models with and without the possibility of antibiotic resistance and used these models as a platform for the evaluation of competing plausible treatment strategies, including changing antimicrobial choice when resistance prevalence surpassed fixed thresholds, random assignment of treatment and use of combination antimicrobial therapy. RESULTS: Absent antimicrobial resistance, strategies that focus on treatment of highest risk individuals (the so-called core group) result in collapse of disease transmission. When antimicrobial resistance exists, a focus on the core group causes rebound in incidence, with maximal dissemination of antibiotic resistance. Random assignment of antimicrobial treatment class outperformed the use of fixed resistance thresholds with respect to sustained reduction in gonorrhoea prevalence. CONCLUSIONS: Gonorrhoea control is achievable only when core groups are treated, but treatment of core groups maximises dissemination of antimicrobial-resistant strains. This paradox poses a great dilemma to the control and prevention of gonorrhoea and underlines the need for gonococcal vaccines.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".