Treatment of asymptomatic bacteriuria during pregnancy: A risk-factor-based approach
Bibliographic record
Abstract
• Asymptomatic bacteriuria during pregnancy can lead to urinary tract infections. • Urinary tract infections in pregnancy are associated with poor perinatal outcomes. • Screen-and-treat programs do not consider pregnant women with low infection risk. • Risk factor-based treatment of asymptomatic bacteriuria during pregnancy should be considered. • A risk factor strategy may reduce the risk of antibiotic resistance, patient and environmental harm. During pregnancy, there is increased risk of ascending urinary tract infection (UTI) resulting in pyelonephritis and associated preterm delivery and low birth weight. It is therefore important that pregnant women who are at high risk of pyelonephritis because of the presence of asymptomatic bacteriuria (ASB) or cystitis receive appropriate antibiotic therapy. The aim of this position paper is to propose a risk factor-based approach for the treatment of ASB and cystitis in pregnancy to help ensure that antibiotic treatment is prescribed only when necessary, and that the benefits of antibiotic treatment outweigh potential harms for pregnant women and neonates. Rather than advocating ASB screen-and-treat for all pregnant women who have ready access to healthcare, this risk factor-based approach involves selective screening for ASB in pregnant women with other risk factors for UTI (previous UTI, diabetes mellitus, urinary tract abnormalities or immunosuppression) and/or a history of preterm birth. Antibiotic treatment is indicated for persistent ASB confirmed with two urine cultures during selective high-risk screening, and for all pregnant women with cystitis confirmed by urine culture, with empiric treatment considered for symptoms of dysuria and urinary frequency. Evidence suggests that such a risk factor-based approach will prevent progression to pyelonephritis in pregnant women with ASB and cystitis while complying with the principles of antimicrobial stewardship.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".