Review and International Recommendation of Methods for Typing Neisseria gonorrhoeae Isolates and Their Implications for Improved Knowledge of Gonococcal Epidemiology, Treatment, and Biology
Bibliographic record
Abstract
Gonorrhea, which may become untreatable due to multiple resistance to available antibiotics, remains a public health problem worldwide. Precise methods for typing Neisseria gonorrhoeae, together with epidemiological information, are crucial for an enhanced understanding regarding issues involving epidemiology, test of cure and contact tracing, identifying core groups and risk behaviors, and recommending effective antimicrobial treatment, control, and preventive measures. This review evaluates methods for typing N. gonorrhoeae isolates and recommends various methods for different situations. Phenotypic typing methods, as well as some now-outdated DNA-based methods, have limited usefulness in differentiating between strains of N. gonorrhoeae. Genotypic methods based on DNA sequencing are preferred, and the selection of the appropriate genotypic method should be guided by its performance characteristics and whether short-term epidemiology (microepidemiology) or long-term and/or global epidemiology (macroepidemiology) matters are being investigated. Currently, for microepidemiological questions, the best methods for fast, objective, portable, highly discriminatory, reproducible, typeable, and high-throughput characterization are N. gonorrhoeae multiantigen sequence typing (NG-MAST) or full- or extended-length porB gene sequencing. However, pulsed-field gel electrophoresis (PFGE) and Opa typing can be valuable in specific situations, i.e., extreme microepidemiology, despite their limitations. For macroepidemiological studies and phylogenetic studies, DNA sequencing of chromosomal housekeeping genes, such as multilocus sequence typing (MLST), provides a more nuanced understanding.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".