Impact of COVID-19 on Group B Streptococcus Colonization Prevalence And Pregnancy Outcomes: A Single-Center Retrospective Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the impact of COVID-19 on the prevalence of group B Streptococcus (GBS) colonization and to examine whether the pandemic has influenced pregnancy complications among women colonized by GBS. METHODS: A retrospective chart review was conducted on 2,448 pregnant women who received care at the Outaouais Birthing Center between 2016 and 2023. Pre- and post-pandemic onset data were compared for GBS positive and negative women. Primary outcomes included termination due to miscarriage, transfers (pre- and post-32 weeks, perinatal, postnatal and newborn), reasons for transfers and newborns' Apgar scores. The secondary outcomes included gestational age at delivery, delivery type and location, newborn birth weight, vaginal birth after cesarean (VBAC) and feeding type. Demographic data were collected to ensure group comparability. RESULTS: GBS prevalence was similar before (29.43 %) and after (26.59 %) COVID-19 onset (p = 0.06), with a significant spike in 2020 (32.95 %, p = 0.009). An inverse relationship was observed between COVID-19 and newborn transfers in the GBS positive group (p < 0.001). Apgar scores below 7 increased during the pandemic (p = 0.006), and reasons for perinatal transfers differed significantly (p = 0.004). In the GBS negative group, postnatal transfers were negatively correlated with COVID-19 (p < 0.001), and transfer reasons post-32 weeks (p = 0.02), perinatal (p < 0.001), and newborn (p = 0.02) transfers differed significantly. CONCLUSION: COVID-19 did not increase the prevalence of GBS in pregnant women. The rise in postpartum transfers and variations in transfer reasons suggest that the pandemic may have influenced healthcare practices rather than directly increasing GBS-related complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".