Uterine artery Doppler velocimetry and obstetric outcomes in connective tissue diseases diagnosed during the first trimester of pregnancy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of connective tissue disease (CTD) diagnosed during the first trimester on uterine arteries (UtA) Doppler velocities and on pregnancy outcomes. METHOD: Pregnant women were screened for CTDs during the first trimester, using a questionnaire, testing for autoantibodies, rheumatologic examination and UtA Doppler evaluations. RESULTS: Out of 3932 women screened, 491 (12.5%) were screened positive at the questionnaire; of them, 165(33.6%) tested positive for autoantibodies, including 66 eventually diagnosed with undifferentiated connective tissue disease (UCTD), 28 with a definite CTD and 71 with insufficient criteria for a diagnosis. Controls were 326 women screened negative for autoantibodies. In logistic analysis, women diagnosed with either UCTD (OR = 7.9, 95% CI = 2.3-27.3) or overt CTD (OR = 24.9, 95% CI = 6.7-92.4), had increased rates of first trimester bilateral UtA notches compared with controls. The rates of bilateral UtA notches persisting in the second (15/94 vs 0/326, p < 0.001) and third trimesters (7/94 vs 0/326, p < .001) were higher among women with CTDs than in controls. The risk of complications (preeclampsia, fetal growth restriction, prematurity, diabetes, fetal loss) was higher (OR = 7.8, 95% CI = 3.6-17.0) among women with CTDs than in controls. CONCLUSION: Women with undiagnosed CTDs have higher rates of bilateral UtA Doppler notches throughout pregnancy and increased rates of adverse pregnancy outcomes than controls.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".