The association between bacteriuria and adverse pregnancy outcomes: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Antibiotics for bacteriuria and urinary tract infection are commonly prescribed during pregnancy to avoid adverse pregnancy outcomes. The aim of this study was to evaluate the association between significant bacteriuria in pregnancy and any of the four pregnancy outcomes: preterm delivery; low birth weight; small for gestational age; and preterm labour. METHODS: Systematic review with meta-analysis of observational studies. We searched PubMed, EMBASE, the Cochrane CENTRAL library, and Web of Science for observational studies published before 1 March 2022. The risk of bias was assessed using the Newcastle-Ottawa scale. Study identification, data extraction and risk-of-bias assessment was performed by two independent authors. We combined the included studies in meta-analyses and expressed results as ORs with 95% CIs (Prospero CRD42016053485). RESULTS: We identified 58 studies involving 421 657 women. The quality of the studies was mainly poor or fair. The pooled, unadjusted OR for the association between any significant bacteriuria and: (i) preterm delivery was 1.62 (95% CI: 1.30-2.01; 27 studies; I2 = 61%); (ii) low birth weight was 1.50 (95% CI: 1.30-1.72; 47 studies; I2 = 74%); (iii) preterm labour was 2.29 (95% CI: 1.53-3.43; 3 studies; I2 = 0%); and (iv) small for gestational age was 1.33 (95% CI: 0.88-2.02; 7 studies; I2 = 54%). Four studies provided an adjusted OR, but were too diverse to combine in meta-analysis. CONCLUSIONS: This systematic review identified an association between significant bacteriuria in pregnancy and the three complications: preterm delivery; low birth weight; and preterm labour. However, the quality of the available evidence is insufficient to conclude whether this association is merely due to confounding factors. There is a lack of high-quality evidence to support active identification and treatment of bacteriuria in pregnancy.
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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".