Molecular epidemiology of<i>Neisseria gonorrhoeae</i>isolates from Saskatchewan, Canada: utility of NG-MAST in predicting antimicrobial susceptibility regionally
Bibliographic record
Abstract
OBJECTIVES: To investigate the molecular epidemiology of isolates of Neisseria gonorrhoeae from Saskatchewan, Canada, using Neisseria gonorrhoeae multi antigen sequence typing (NG-MAST), and to assess associations between antimicrobial susceptibility (AMS) and specific strain types (STs). METHODS: 320 consecutive gonococcal isolates, collected between 2003 and 2008, were typed by NG-MAST. STs were grouped if one of their alleles was common and the other differed by ≤1% in DNA sequence. AMS was determined by agar dilution (CLSI) to seven antibiotics. RESULTS: N gonorrhoeae isolates were resolved into 82 individual NG-MAST STs and 18 NG-MAST ST groups with groups 25, 3655, 921, 3654, 3657 and 3656 comprising 53.4% (171/320) of the isolates. N gonorrhoeae isolates susceptible to all the tested antimicrobials were significantly (p<0.05) associated with ST 25 (87%). Other significant associations between ST and AMS included: ST 3654 and isolates with minimum inhibitory concentrations of ≥0.03 mg/L to third generation cephalosporins; ST 3711 (100%) and TRNG; and ST/group 3654 (43%) and chromosomal resistance to penicillin and tetracycline. Several NG-MAST STs/groups were significantly associated with isolates with chromosomal resistance to tetracycline. Isolates resistant to ciprofloxacin (n=5) and azithromycin (n=2) appeared as individual STs. Significant associations were observed among individual STs, sex and age of the patient, and regional and temporal distributions. CONCLUSIONS: Associations between N gonorrhoeae AMS and NG-MAST STs were identified and may be useful in predicting AMS regionally. Because STs in different countries vary considerably, the use of NG-MAST for the prediction of AMS globally requires further study.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".