Screening and Treating Chlamydia trachomatis Genital Infection to Prevent Pelvic Inflammatory Disease
Bibliographic record
Abstract
We critically reviewed randomized controlled trials evaluating chlamydia screening to prevent pelvic inflammatory disease (PID) and explored factors affecting interpretation and translation of trial data into public health prevention. Taken together, data from these trials offer evidence that chlamydia screening and treatment is an important and useful intervention to reduce the risk of PID among young women. However, the magnitude of benefit to be expected from screening may have been overestimated based on the earliest trials. It is likely that chlamydia screening programs have contributed to declines in PID incidence through shortening prevalent infections, although the magnitude of their contribution remains unclear. Program factors such as screening coverage as well as natural history factors such as risk of PID after repeat chlamydia infection can be important in determining the impact of chlamydia screening on PID incidence in a population. Uptake of chlamydia screening is currently suboptimal, and expansion of screening among young, sexually active women remains a priority. To reduce transmission and repeat infections, implementation of efficient strategies to treat partners of infected women is also essential. Results of ongoing randomized evaluations of the effect of screening on community-wide chlamydia prevalence and PID will also be valuable.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".