Successful integration of HIV pre-exposure prophylaxis into a community-based HIV prevention program for female sex workers in Kolkata, India
Bibliographic record
Abstract
We assessed the impact of pre-exposure prophylaxis (PrEP) in the context of a community-based HIV program among female sex workers (FSWs) in Kolkata, India. This was an open-label, uncontrolled demonstration trial. HIV seronegative FSWs over 18 years were eligible. Participants were administered daily tenofovir/emtricitabine (TDF-FTC) with follow-up visits at months 1, 3, 6, 9, 12, and 15. Drug adherence was monitored by self-report, and a random subset of participants underwent plasma TDF testing. 843 women were screened and 678 enrolled and started on PrEP. Seventy-nine women (11%) did not complete all scheduled visits: four women died of reasons unrelated to PrEP and 75 withdrew, for a 15-month retention rate of 89%. Self-reported daily adherence was over 70%. Among those tested for TDF, the percentage of women whose level reached ≥40 ng/mL was 65% by their final visit. There were no HIV seroconversions, and no evidence of significant changes in sexual behavior. This study demonstrated the feasibility and effectiveness of PrEP for FSWs in Kolkata, with very high levels of adherence to PrEP and no HIV seroconversions. The integration of PrEP into an existing community-based HIV prevention program ensured community support and facilitated adherence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".