Copper intrauterine device increases vaginal concentrations of inflammatory anaerobes and depletes lactobacilli compared to hormonal options in a randomized trial
Bibliographic record
Abstract
Abstract Effective contraceptives are a global health imperative for reproductive-aged women. However, there remains a lack of rigorous data regarding the effects of contraceptive options on vaginal bacteria and inflammation. Among 218 women enrolled into a substudy of the ECHO Trial (NCT02550067), we evaluate the effect of injectable intramuscular depot medroxyprogesterone acetate (DMPA-IM), levonorgestrel implant (LNG), and a copper intrauterine device (Cu-IUD) on the vaginal environment after one and six consecutive months of use, using 16S rRNA gene sequencing and multiplex cytokine assays. Primary endpoints include incident BV occurrence, bacterial diversity, and bacterial and cytokine concentrations. Secondary endpoints are bacterial and cytokine concentrations associated with later HIV seroconversion. Participants randomized to Cu-IUD exhibit elevated bacterial diversity, increased cytokine concentrations, and decreased relative abundance of lactobacilli after one and six months of use, relative to enrollment and other contraceptive options. Total bacterial loads of women using Cu-IUD increase 5.5 fold after six months, predominantly driven by increases in the concentrations of several inflammatory anaerobes. Furthermore, growth ofL. crispatus(MV-1A-US) is inhibited by Cu2+ions below biologically relevant concentrations, in vitro. Our work illustrates deleterious effects on the vaginal environment induced by Cu-IUD initiation, which may adversely impact sexual and reproductive health.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".