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Record W3133322354 · doi:10.1177/0956462420983992

Successful integration of HIV pre-exposure prophylaxis into a community-based HIV prevention program for female sex workers in Kolkata, India

2021· article· en· W3133322354 on OpenAlexaff
Smarajit Jana, Protim Ray, Soma Roy, Abhijit Kadam, Raman Gangakhedkar, Bharat Bhushan Rewari, Stephen Moses, Marissa Becker

Bibliographic record

VenueInternational Journal of STD & AIDS · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Manitoba
FundersBill and Melinda Gates Foundation
KeywordsMedicinePre-exposure prophylaxisHuman immunodeficiency virus (HIV)Context (archaeology)EmtricitabineMen who have sex with menFamily medicineViral loadAntiretroviral therapySyphilis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.378
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

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