Incident Syphilis Infections in an HIV Population: Presentation and Care
Bibliographic record
Abstract
Syphilis is an emerging global health concern with an estimated 12 million new infections being documented annually. In individuals with HIV, syphilis presentations may be atypical leading to the recommendation for regular routine screening. This retrospective cohort study aimed to characterize syphilis presentation and serologic features in an HIV co-infected population. All incident syphilis infections (initial and repeat) occurring in HIV patients between 2006 and 2016 were identified via routine syphilis screening every four months during care at Southern Alberta HIV Clinic (SAC) and Calgary STI clinic (CSTI). Charts and databases were reviewed for each syphilis event. Data was pooled and statistical analysis performed. We identified 361 infections, of which 250 cases met inclusion criteria in 195 different HIV patients (72% initial and 28% repeat infections). Infections were seen more commonly in men 227/250 (95%). Caucasians accounted for 72% of the population with 13% being within the African, Caribbean and Black community. The risk factors for HIV in patients with syphilis were MSM 76%, heterosexual activity 19% and intravenous drug use 4%. Over half (50.8%) of syphilis infections were asymptomatic and only identified through routine/risk based screening. Rash was the most common symptomatic presentation (23%), followed by skin lesions (18%). Those with repeat syphilis infections, not on ART and CD4 counts less than 200 cells/mm3 were more likely to be symptomatic. Ten patients (4%) experienced CNS syphilis involvement and all presented with an initial RPR titer of 31:32. The RPR titers were higher among those with repeat syphilis infections (29% had titers over 1:256). Those with repeat episodes of syphilis usually had primary (28%), secondary (28%) or early latent disease (39%) and rarely late latent disease (3%). Since initiation of screening, the amount of late latent syphilis among our HIV patients has decreased from 50% to 4.4%. Syphilis infection was most often asymptomatic in our HIV positive population and occurred predominantly in MSM. Our findings stress the importance of regular syphilis screening among high-risk populations. All authors: No reported disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".