Transient ischemic attack and pregnancy, delivery and neonatal outcomes—An evaluation of a population database
Bibliographic record
Abstract
OBJECTIVE: Transient ischemic attack (TIA) is rare in women of reproductive age. We aimed to compare perinatal outcomes between women who suffered from a TIA to those who did not. METHODS: A retrospective population-based cohort study utilizing the Healthcare Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS). All women who delivered or had a maternal death in the US (2004-2014) were included in the study. Pregnancy, delivery, and neonatal outcomes were compared between women with an ICD-9 diagnosis of a TIA to those without. RESULTS: Overall, 9 096 788 women met the inclusion criteria. Of these, 203 women (2.2/100000) had a TIA (either before or during pregnancy). Women with TIA, compared to those without, were more likely to be older than 35 years of age, white, in the highest income quartile, be insured by private insurance and suffer from obesity and chronic hypertension. Patients in the TIA group, compared to those without, had a higher rate of pregnancy-induced hypertension (aOR 2.5, 95% CI: 1.55-4.05, P < 0.001), pre-eclampsia (aOR 3.77, 95% CI: 2.15-6.62, P < 0.001), eclampsia (aOR 28.05, 95% CI: 6.91-113.95, P < 0.001), preterm delivery (aOR 1.78, 95% CI: 1.03-3.07, P = 0.039), and maternal complications such as deep vein thrombosis (aOR 33.3, 95% CI: 8.07-137.42, P < 0.001). Regarding neonatal outcomes, patients with a TIA, compared to those without, had a higher rate of congenital anomalies (aOR 7.04, 95% CI: 2.86-17.32, P < 0.001). CONCLUSION: Women with a TIA diagnosis before or during pregnancy had a higher rate of maternal complications, including hypertensive disorders of pregnancy and venous thromboembolism, as well as an increased risk of congenital anomalies.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".