Obstetrical and Neonatal Outcomes in Women With Gestational Lyme Disease [ID: 1354384]
Bibliographic record
Abstract
INTRODUCTION: The incidence of Lyme disease (LD) infections has risen in recent decades. Gestational LD has been associated with adverse pregnancy outcomes; however, the results have been contradictory. The objective of this study was to examine the effects of gestational LD on obstetrical and neonatal outcomes. METHODS: Using the Healthcare Cost and Utilization Project–National Inpatient Sample database from the United States, we conducted a retrospective cohort study of pregnant women who were admitted to hospital between 2016 and 2019. The exposed group consisted of pregnant women with gestational LD infection (ICD-10 code of A692x), whereas the comparison group consisted of pregnant women without gestational LD. Descriptive statistics and multivariate logistic regression models, adjusted for baseline maternal characteristics, were used to determine the associations between gestational LD and obstetrical and neonatal outcomes. RESULTS: Our cohort included 2,943,575 women, 226 of whom were diagnosed with LD during pregnancy. The overall incidence of gestational LD was 7.67 per 100,000 pregnancy admissions. The incidence of gestational LD was stable during the study period. Women with gestational LD were more likely to be Caucasian, older, have private health insurance, and earn higher incomes than the comparison group. Gestational LD was associated with an increased risk of placental abruption (adjusted odds ratio [aOR] 3.45, 95% CI 1.53–7.80) and preterm birth (aOR 1.58, 95% CI 1.03–2.42). CONCLUSION: Gestational LD is associated with a higher risk of placental abruption and preterm birth. Pregnancies complicated by LD may benefit from being closely monitored in tertiary care settings.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".