The impact of syndromic treatment of sexually transmitted diseases on genital shedding of HIV-1
Bibliographic record
Abstract
OBJECTIVES: To examine the impact of sexually transmitted diseases (STD) syndromic treatment on genital shedding of HIV and the impact among women in whom STD treatment was not successful. DESIGN: Seventy-one HIV-infected women were included; 60 had symptomatic STD [72% with genital discharge syndrome (GDS) and 28% with genital ulcer syndrome (GUS)] and 11 controls did not have symptomatic STD. Cervical HIV load in 94% women was measured at baseline and after STD treatment. RESULTS: Cervical HIV load at entry was significantly higher in women with symptomatic STD than in controls [median, 3.15; interquartile range (IQR), 1.90-3.34 versus median, 1.90; IQR, 1.90-2.19 log10 RNA copies/swab, respectively; P = 0.024]. Women with STD were also more likely to have detectable cervical HIV RNA (68% versus 27%; P = 0.016). Cervical HIV load was significantly higher in women with GUS than in those with GDS (median 3.46; IQR, 2.84-4.18 versus median, 2.83; IQR, 1.90-3.31 log10 copies/swab; P = 0.019). There was no significant reduction in genital HIV shedding after syndromic treatment of GDS or GUS. However, significant decreases were limited to only those with clinical improvement (median, 2.91; IQR, 1.90-3.45 versus median, 2.25; IQR, 1.90-3.08 log10 RNA copies/swab, respectively; P = 0.006). GUS was significantly associated with treatment failure, independent of plasma HIV RNA load and CD4 T-cell count (odds ratio, 4.79; 95% confidence interval, 1.32-17.46). CONCLUSIONS: The fact that STD syndromic treatment impacts very little in reducing genital HIV shedding underscores the need for appropriate validation of STD syndromic diagnosis and management to control heterosexual transmission of HIV.
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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 | 0.000 | 0.000 |
| 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".