Reproductive Outcomes in Women With Congenital Uterine Anomalies Detected by Three-Dimensional Ultrasound Screening
Bibliographic record
Abstract
In Brief OBJECTIVE To determine reproductive outcomes in women with congenital uterine anomalies detected incidentally by three-dimensional ultrasound. METHODS We studied 1089 women with no history of infertility or recurrent miscarriage who were seen for a transvaginal ultrasound scan. They were screened for uterine abnormalities using three-dimensional ultrasound. We determined prevalence of miscarriage and preterm labor in women with normal and abnormal uterine morphology. RESULTS We found that 983 women had a normally shaped uterine cavity, 72 an arcuate, 29 a subseptate, and five a bicornuate uterus. Women with a subseptate uterus had a significantly higher proportion of first-trimester loss (Z = 4.68, P < .01) compared with women with a normal uterus. Women with an arcuate uterus had a significantly greater proportion of second-trimester loss (Z = 5.76, P < .01) and preterm labor (Z = 4.1, P < .01). There were no other significant differences in pregnancy outcomes between women with normal and abnormal uterine morphology. CONCLUSION This study shows the potential value of three-dimensional ultrasound and confirmed that women with congenital uterine anomalies were more likely to have adverse pregnancy outcomes than women with a normal uterus. Women with a diagnosis of congenital uterine anomaly have more adverse reproductive outcomes.
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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".