Deciphering the serological response to syphilis treatment in men living with HIV
Bibliographic record
Abstract
OBJECTIVE: To examine syphilis serology after treatment in people living with HIV. No unanimous guidelines exist in the era of increasing coinfection. DESIGN: Retrospective review using a tertiary care clinic in Toronto from 2000 to 2017. METHODS: The 2015 Centers for Diseases Control and Prevention syphilis guidelines were used to define an adequate serologic response. Cumulative distribution estimates and proportional hazards models accounting for interval censoring estimated the time to serologic response and seroreversion. Multistate models were used to investigate extended periods of serofast serology. RESULTS: A total of 171 patients with syphilis met our inclusion criteria (16 primary, 53 secondary, 26 early latent, 46 late latent, 30 neurosyphilis). Serologic response was achieved by 12 months for 65 (94%) patients and by 12-18 months for four (6%) patients with primary/secondary syphilis. For latent and neurosyphilis, 94 (92%) achieved serologic response by 24 months and one (1%) at 24.1 months. 84 (49%) patients achieved seroreversion with a median (95% confidence interval) time of 2 (1.44, 2.68) years. Latent syphilis was associated with a lower likelihood of achieving serologic response [hazard ratio (HR) = 0.52, P = 0.05] and seroreversion (HR = 0.27, P < 0.001) compared with primary/secondary syphilis. The probability of moving from a new infection state to a serofast state within 1 year was high (0.65) but the 1-year probability of transitioning from a serofast state to seroreversion was low (0.27). CONCLUSION: The majority of people living with HIV infected with syphilis will achieve an adequate serologic response as per the Centers for Diseases Control and Prevention guidelines. Seroreversion was observed in about half but can take years to occur.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".