P1-S1.05 The detection rate of<i>Chlamydia trachomatis</i>and<i>Mycoplasma genitalium</i>infections in STD clinics in Novosibirsk, Russian Federation
Bibliographic record
Abstract
Background Currently, in Russia, the incidence of syphilis, gonorrhoea, chlamydia, trichomoniasis, urogenital herpes, and anogenital warts are officially registered. However, statistical records and reporting forms do not include mycoplasma infections (eg, Mycoplasma genitalium ). Methods The aim of the present study was to evaluate the detection rates of Chlamydia trachomatis and M genitalium infections in patients who had attended to STD clinics in Novosibirsk in 2009–2010. A total of 9208 and 13 006 patients were examined for M genitalium and C trachomatis , respectively, in different settings (antenatal clinics, hospitals, health centers, STI clinics). Both infections were tested in urethral and/or cervical swabs with nucleic acid amplification techniques (“Litex” and “DNA technology”, Russia). Results The detection rates of M genitalium and C trachomatis had not changed over 2009 and 2010, accounting to 12.6–12.6%, and 12.9–13.0%, respectively. Coinfection was observed in only 0.55% of examinies. However, seasonal variations showed different patterns for these two infections (Abstract P1-S1.05 figure 1). Statistical analysis by month revealed that the highest rates of M genitalium were reported in February and March, and the lowest ones—in July. Monthly analysis found even distribution of infection with C trachomatis along a year, while the lowest incidence was found in July. Abstract P1-S1.05 Figure 1 Detection rates of Mycoplasma genitalium and Chlamydia trachomatis according to the attendance data (by month in %). Conclusions The incidence rates of C trachomatis and M genitalium are approximately the same and account for 12–13% among men and women, equally. The combination of these infections is rare (0.55%). During 2009–2010, there are parallel trends in the detection of these two infections, but in September the reciprocal event was shown. This discrepancy may be due to the peculiarities of various microorganisms, and the clinical signs of the infection—chlamydia can cause non-gonococcal urethritis, which is the reason for examination, and mycoplasma infection is mainly symptomless. Nevertheless, a hypothesis of various patterns of infections prevalence around a year in northern countries (ie, Canada, Alaska, Scandinavia, Russia) needs further clarification. The post-holiday period (September–October) may be a crucial point in the activity of the infectious process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".