Antimicrobial resistance markers as a monitoring index of gonorrhoea in Thailand
Bibliographic record
Abstract
Multiplex PCR was applied to explore the antimicrobial-resistance profiles of 145 gonococci isolated from Bangrak Hospital, Thailand in 2007. All isolates were clearly identified for the plasmid-mediated resistant types of penicillin (Asia, Africa and Toronto) and tetracycline (American and Dutch). This method can also predict the decreased susceptibility to ciprofloxacin by detection of Ser-91 mutation. Prevalence rates of penicillinase-producing Neisseria gonorrhoeae (PPNG) and high-level tetracycline-resistance N. gonorrhoeae (TRNG) were shown to be high as 82.1% and 84.1%, respectively. Most PPNG carried the Africa-type (78.2%) while the American-type (61.8%) was harboured in most TRNG. Mono- and triple-resistance patterns were presented in 2.6% and 79.5% of male, 20.7% and 62.1% of men who have sex with men (MSM), 0% and 75.0% of female, and 10% and 70% of female sex workers (FSW). Additionally, the rate of the Dutch type was high in patients among the age of 35-44 years (57.1%) and female patients (43.8%). The changing types of plasmids have been noticed during the time period of study. The multi-resistance patterns of the gonococcal isolates can be used as an epidemiological index of gonorrhoea and human sexual behaviours. This information will support the management of individual patients as well as the public health surveillance.
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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 | 0.002 |
| 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) | 0.000 | 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".