HIV pre-exposure prophylaxis during the SARS-CoV-2 pandemic
Bibliographic record
Abstract
\(\bf Aims:\) Since 2017, HIV pre-exposure prophylaxis (PrEP) care has been provided through an intersectoral collaboration at WIR (Walk-in-Ruhr, Center for Sexual Health and Medicine, Bochum, Germany). The aim of this study was to establish possible impact of COVID-restrictions on the sexual behavior of PrEP users in North Rhine-Westphalia. \(\bf Methods:\) The current PrEP study collected data of individuals using PrEP, their sexual behavior and sexually transmitted infections (STIs) before (each quarter of year 2018) and during the COVID-19 pandemic (each quarter of year 2020). \(\bf Results:\) During the first lockdown in Germany from mid-March until May 2020, PrEP-care appointments at WIR were postponed or canceled. Almost a third of PrEP users had discontinued their PrEP intake in the \(2^{nd}\) quarter of 2020 due to alteration of their sexual behavior. The number of sexual partners decreased from a median of 14 partners in the previous 6 months in \(1^{st}\) quarter of 2020, to 7 partners in \(4^{th}\) quarter of 2020. Despite such a significant reduction in partner number during the pandemic in comparison to the pre-pandemic period, a steady rate of STIs was observed among PrEP users in 2020. \(\bf Conclusion:\9 The SARS-CoV-2-pandemic has impacted PrEP-using MSM in North Rhine-Westphalia with respect to their PrEP intake regimen and sexual behavior in 2020. Our study revealed a steady rate of STI among PrEP users even during the pandemic, thus highlighting the importance of ensuring appropriate HIV/STI prevention services in times of crisis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".