Perinatal morbidity and mortality associated with chlamydial infection: a meta-analysis study
Bibliographic record
Abstract
To evaluate the effect of Chlamydia trachomatis infection during pregnancy on perinatal morbidity and mortality. Systematic review and meta-analysis in an electronic database and manual, combining high sensitivity specific descriptors seeking to answer the research objective. The articles considered to be of high methodological quality (score above 6 on the Newcastle-Ottawa Scale) were assessed by meta-analysis. Summary estimates of 12 studies were calculated by means of Mantel-Haenszel test with 95% confidence interval. It was observed that Chlamydia infection during pregnancy increased risk of preterm labor (relative risk (RR) = 1.35 [1.11, 1.63]), low birth weight (RR = 1.52 [1.24, 1.87]) and perinatal mortality (RR = 1.84 [1.15, 2.94]). No evidence of increased risk was associated with Chlamydia infection in regard to premature rupture of membranes (RR = 1.13 [0.95, 1.34]), abortion and postpartum endometritis (RR = 1.20 [0.65, 2.20] and 0.89 [0.49, 1.61] respectively). The diagnosis and treatment of Chlamydia cervicitis during pregnancy can reduce perinatal morbidity and mortality associated with this infection. However, clinical trials are needed to confirm these findings.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".