HIV pre-exposure prophylaxis: It is time to consider harm reduction care for adolescents in Canada
Bibliographic record
Abstract
Youth (aged 15 to 29 years) account for one quarter of new HIV cases in Canada. Of those, men-who-have-sex-with-men make up one third to one half of new cases in that age range. Moreover, Indigenous youth are over-represented in the proportion of new cases. The use of emtricitabine/tenofovir disoproxil fumarate as pre-exposure prophylaxis (PrEP) significantly reduces the risk of HIV acquisition in adults. Its use was expanded to include youth over 35 kg by the U.S. Food and Drug Administration in 2018. However, PrEP uptake remains low among adolescents. Prescriber-identified barriers include lack of experience, concerns about safety, unfamiliarity with follow-up guidelines, and costs. This article provides an overview of PrEP for youth in Canada, and its associated safety and side effect profiles. Hypothetical case vignettes highlight some of the many demographics of youth who could benefit from PrEP. We present a novel flow diagram that explains the baseline workup, prescribing guidelines, and follow-up recommendations in the Canadian context. Additional counselling points highlight some of the key discussions that should be elicited when prescribing PrEP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".