Empiric antibiotics for peripartum bacteremia: A chart review from a quaternary Canadian centre
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of empiric antibiotic protocols for peripartum bacteremia at a quaternary institution by describing incidence, microbial epidemiology, clinical source of infection, susceptibility patterns, and maternal and neonatal outcomes. METHODS: Retrospective chart review of peripartum patients with positive blood cultures between 2010 and 2018. RESULTS: The incidence of peripartum bacteremia was 0.3%. The most cultured organisms were Escherichia coli (51, 26.7%), Streptococcus spp. (52, 27.2%), and anaerobic spp. (35, 18.3%). Of the E. coli cases, 54.9% (28), 19.6% (10), and 19.6% (10) were resistant to ampicillin, first- and third-generation cephalosporins, respectively. Clinical sources of infection included intra-amniotic infection/endometritis (115, 67.6%), upper and/or lower urinary tract infection (23, 13.5%), and soft tissue infection (8, 4.7%). Appropriate empiric antibiotics were prescribed in 137 (83.0%) cases. There were 7 ICU admissions (4.2%), 18 pregnancy losses (9.9%), 9 neonatal deaths (5.5%), and 6 cases of neonatal bacteremia (3.7%). CONCLUSION: Peripartum bacteremia remains uncommon but associated with maternal morbidity and neonatal morbidity and mortality. Current empiric antimicrobial protocols at our site remain appropriate, but continuous monitoring of antimicrobial resistance patterns is critical given the presence of pathogens resistant to first-line antibiotics.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".