Pre-exposure prophylaxis in a new era of HIV prevention
Bibliographic record
Abstract
Linda is a healthy, 25-year-old married woman living in the city of Toronto who presents with questions about human immunodeficiency virus (HIV) prevention. Linda and her husband met on an online dating forum in 2011 and married each other one year later while they were both seronegative. She describes her and her husband as having an “open” marriage and have had 2 and 5 extramarital partners, respectively. In 2014, Linda’s husband was incidentally diagnosed with HIV infection after donating blood at a clinic, and Linda received testing shortly thereafter which came back negative for HIV. His viral load had reached 20 000 copies/mL and he immediately began highly active antiretroviral therapy (HAART). His infection has stabilized with undetectable viral loads and a CD4+ T-helper cell count of 1000 cells/μL; however, the risk of transmission has created damaging tensions within their marriage. Prior to his diagnosis Linda reports condom usage of less than 50% for both herself and her husband; however, the anxiety and fear of infection has prompted usage of over 90%. Linda wants to be at ease in her sexual endeavours but is motivated to remain uninfected so she can be healthy to take care of her husband. She read about a new drug on Twitter that can prevent HIV infection in non-infected people and requests your counsel on it. She has an unremarkable history with respect to sexually transmitted infections (STIs), drug use, smoking and hepatitis, and has had no recent symptoms of fever, malaise, or sore throat.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".