Sexually Transmitted Infection Positivity Rate and Treatment Uptake Among Female and Male Sexual Assault Victims Attending The Amsterdam STI Clinic Between 2005 and 2016
Bibliographic record
Abstract
BACKGROUND: Victims could become infected with sexually transmitted infections (STIs) during a sexual assault. Several guidelines recommend presumptive antimicrobial therapy for sexual assault victims (SAVs). We assessed the STI positivity rate and treatment uptake of female and male SAVs at the Amsterdam STI clinic. METHODS: Sexual assault victims answered assault-related questions and were tested for bacterial STI (chlamydia, gonorrhea, and syphilis), hepatitis B, and HIV during their initial visits. Sexual assault victim characteristics were compared with non-SAV clients. Backward multivariable logistic regression analysis was conducted to assess whether being an SAV was associated with a bacterial STI. The proportion of those returning for treatment was calculated. RESULTS: From January 2005 to September 2016, 1066 (0.6%) of 168,915 and 135 (0.07%) of 196,184 consultations involved female and male SAVs, respectively. Among female SAVs, the STI positivity rate was 11.2% versus 11.6% among non-SAVs (P = 0.65). Among male SAVs, the STI positivity rate was 12.6% versus 17.7% among non-SAVs (P = 0.12). In multivariable analysis, female SAVs did not have increased odds for an STI (odds ratio 0.94; 95% confidence interval, 0.77-1.13), and male SAVs had significantly lower odds for an STI (odds ratio, 0.60; 95% confidence interval, 0.36-0.98). Of SAVs requiring treatment, 89.0% (female) and 92.0% (male) returned. CONCLUSIONS: The STI positivity rate among female SAVs was comparable with female non-SAVs, but male SAVs had lower odds for having a bacterial STI than did male non-SAVs, when adjusting for confounders. The return rate of SAV for treatment was high and therefore does not support the recommendations for presumptive therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".