Impact of a carrageenan gel on viral load of genital human papillomavirus infections in sexually active women: Findings from the Carrageenan‐gel Against Transmission of Cervical Human papillomavirus (CATCH) trial
Bibliographic record
Abstract
Previous research has shown that women's use of a carrageenan gel reduces the risk of acquiring genital human papillomavirus (HPV) infections but does not help to clear existing ones. Although gel use may not result in complete clearance, it may decrease the viral load of HPV infections. We tested this hypothesis in the Carrageenan-gel Against Transmission of Cervical Human papillomavirus (CATCH) randomized controlled trial. Participants of the CATCH study were selected for viral load testing if they had completed the first four study visits and tested positive for HPV42 or HPV51 in at least one of these visits. HPV42 and HPV51 were chosen as they were among the most abundant low- and high-risk types, respectively, in the study sample. We measured viral load with a type-specific real-time polymerase chain reaction. Results were displayed using summary statistics. Of 461 enrolled participants, 39 were included in the HPV42 analysis set and 56 in the HPV51 analysis set. The median time between visits 1 and 4 was 3.7 months. The viral load (copies/cell) of HPV42 ranged from <0.001 to 13 434.1, and that of HPV51 from <0.001 to 967.1. The net median change in HPV42 viral load over all four visits was -1.04 copies/cell in the carrageenan and -147 copies/cell in the placebo arm (Wilcoxon rank sum test, p = 0.26). There was no net median change in HPV51 viral load over all four visits in either arm (p = 0.45). The use of a carrageenan-based gel is unlikely to reduce the viral load of HPVs 42 or 51.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".