Bibliographic record
Abstract
### What you need to know A 22 year old man attends a sexual health clinic. Six months previously, he completed a course of HIV post-exposure prophylaxis for anal receptive intercourse without a condom. Since then, he reports six anal receptive sexual exposures without a condom. He has been treated at another sexual health clinic for rectal gonorrhoea. He asks if you could prescribe him PrEP. HIV pre-exposure prophylaxis (PrEP) is the use of HIV antiretroviral medicines in people without HIV to prevent infection. When taken correctly, PrEP has been shown to reduce the risk of HIV infection in numerous populations, including young women, men who have sex with men, HIV uninfected members of sero-discordant couples, and injecting drug users.123 Based on this evidence, PrEP is recommended for people considered at high risk of acquiring HIV. However, availability of PrEP and access to expert advice and counselling about its use vary globally. For example, in England it is only available as part of a clinical trial, whereas it is available in the rest of the UK at sexual health clinics. Awareness of PrEP among at-risk groups is growing, and availability of the treatment is becoming more widespread; therefore generalists—as well as those working in sexual health services—require an awareness of the indications, efficacy, use, and potential harms of PrEP. ### Identify risk of HIV Ask the patient about known or potential HIV exposures in the previous six months, particularly sexual exposures or injecting drug …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.037 |
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".