Clinical and demographic trends in a sexually transmitted infection clinic in Mumbai (1994-2006): An epidemiologic analysis
Bibliographic record
Abstract
BACKGROUND: People presenting to sexually transmitted infections (STIs) clinics represent an important risk group for HIV infection; prevention strategies will depend on the clinical attendance. AIMS: The demographic and clinical changes in clinic attendees in Mumbai, as well as the factors associated with HIV infection in this clinic over a 13-year period, were assessed. METHODS: STI clinic data in 3417 individuals (1994 to 2006) were analyzed: clinical presentation, types of STIs, and serology over the 13-year period. We used a logistic regression model to assess socio-demographic and clinical associations with HIV infection. RESULTS: The clinic evaluated 689 patients in 1994 and the number had dropped to 97 in 2006. In 1994, the majority of STIs seen in the clinic were bacterial (53%, 95% confidence interval [CI] 50% to 57%); however, this proportion had dropped in 2006 (28%, 95% CI: 19% to 38%). There was a proportional increase in viral STIs during the same time period. Although women attending the clinic were younger than men, they were more likely to be married. The overall seropositivity for HIV was 28%. Viral STIs were more likely to be associated with HIV than bacterial infections (odds ratio: 1.5, 95% CI: 1.2 to 1.9). CONCLUSIONS: Viral infections were the most common STIs in recent years in a tertiary care center in Mumbai. HIV prevalence was high in this population. Thus, these clinical data suggest that STI patients were and continue to be an important group for HIV prevention in the country.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".