Obstetrical and neonatal outcomes in women with gestational Lyme disease
Bibliographic record
Abstract
OBJECTIVE: 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 study objective was to examine the effects of gestational LD on obstetrical and neonatal outcomes. METHODS: Using the Healthcare Cost & Utilization Project National (Nationwide) Inpatient Sample from the United States, we conducted a retrospective cohort study of pregnant patients admitted to the hospital between 2016 and 2019. The exposed group consisted of pregnant patients with gestational LD infection (International Classification of Diseases, Tenth Revision [ICD-10] code A692x), while the comparison group consisted of pregnant patients 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: The cohort included 2 943 575 women, 226 of whom were diagnosed with LD during pregnancy. The incidence of LD was 7.67 per 100 000 pregnancy admissions. The incidence of gestational LD was stable over the study period. Pregnant patients with LD were more likely white, older, to have private health insurance, and to belong to a higher income quartile. Gestational LD was associated with an increased risk of placental abruption (adjusted odds ratio [aOR], 3.45 [95% confidence interval (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, while associated with a higher risk of certain adverse outcomes, can be followed in most healthcare settings.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".