<i>Neisseria gonorrhoeae</i> Sequence Types During an Increase of Gonorrhea Among Young Women in 2022 and 2023 in the Netherlands
Bibliographic record
Abstract
Abstract Background Since September 2022, an ongoing increase in gonorrhea positivity rates and diagnoses among young women visiting sexual health centers was observed in the Netherlands. To gain more insight into possible drivers of this increase, this study aimed to investigate the molecular diversity, clustering, and transmission of Neisseria gonorrhoeae related to the gonorrhea increase in the Netherlands. Methods Between January 2022 and March 2023, viable and stored N gonorrhoeae isolates from vaginal swab samples from 273 women <25 years of age were included from 4 sexual health centers in the Netherlands. All isolates were subjected to whole genome sequencing. The molecular diversity was investigated by analyzing (core genome) multilocus sequence types (STs) over time and by patient characteristics. Results The prevalence of ST7359 and ST7822 increased from <5% in the beginning of 2022 to ∼23% in 2023 and ST9363 from 6% to 14%. ST7359 and ST9363 were associated with hosts with university-level education and Dutch ethnicity. ST7359 and ST7822 also included large clusters of genetically related isolates (<6-gene difference). No indication for reduced antimicrobial susceptibility to ceftriaxone was found in any ST. Conclusions The N gonorrhoeae surge among young women is not caused by a single strain. The prevalence of STs shifted over time, and certain STs were associated with distinct populations and showed clustering of genetically related isolates. These findings suggest rapid clonal expansion of several circulating STs. This may be indicative of increased transmission within specific sexual networks.
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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 | 0.001 |
| 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 | 0.000 |
| 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".