HIV nonoccupational postexposure prophylaxis for sexual assault cases: a 3-year investigation
Bibliographic record
Abstract
OBJECTIVE: Nonoccupational postexposure prophylaxis (nPEP) programs offer antiretroviral therapy to prevent HIV following at-risk exposures like sexual assault. We investigated the levels of elective nPEP uptake among sexual assault cases presenting for emergency medical care. DESIGN: Retrospective analysis. METHODS: The analysis included over 3 years (1 January 2015 to 30 September 2018) of clinic information from the Sexual Assault and Partner Abuse Care Program (SAPACP) at The Ottawa Hospital, the regional emergency department care point following sexual assault. Descriptive analyses assessed the number of cases eligible for nPEP and those who started nPEP. Bivariable/multivariable logistic regression modelling assessed factors most strongly associated with starting nPEP using odds ratios (OR), adjusted OR (AOR), and 95% confidence intervals (CI). RESULTS: The SAPACP saw 1712 patients; 1032 were sexual assault cases, 494 were eligible for nPEP, and 307/494 (62%) eligible patients started nPEP. The median age was 23 years (IQR: 20-31), with 446 (90%) cases being female. There were 86 (17%) cases who arrived by ambulance, and 279 (56%) assaults involving a known assailant. Reduced odds of starting nPEP were observed among female cases (AOR: 0.44, 95% CI: 0.21-0.93), those who arrived by ambulance (AOR: 0.56, 95% CI: 0.35-0.91), and those with a known assailant (AOR: 0.56, 95% CI: 0.36-0.78). CONCLUSION: We found that 62% of eligible sexual assault cases started nPEP. Key groups most likely to decline nPEP included female cases, those who arrived by ambulance, and those with known assailants. Providers can use these findings to provide recommendations to sexual assault survivors most likely to decline nPEP, yet still in need of care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".