Radiologic Approaches In The Detection And Monitoring of Multiple Pregnancy Complications
Bibliographic record
Abstract
Multiple pregnancy, defined as the gestation of two or more fetuses, is a high-risk condition asso- ciated with increased maternal and fetal morbidity and mortality. The global incidence varies geo- graphically, with Africa particularly Nigeria recording the highest rates. Twin pregnancies consti- tute over 98% of multiple gestations, and their risk profile intensifies with the number of fetuses. Complications such as preterm birth, fetal growth restriction, twin-to-twin transfusion syndrome (TTTS), and maternal hypertensive disorders are common. Radiology plays a pivotal role in the diagnosis, monitoring, and management of multiple pregnancies. Ultrasound remains the corner- stone imaging modality due to its safety, accessibility, and real-time imaging capability. It is indis- pensable in determining chorionicity, detecting congenital anomalies, and assessing fetal growth and wellbeing. Advanced modalities, including high-resolution ultrasound, 3D/4D imaging, Dop- pler, and fetal MRI, have further enhanced diagnostic accuracy, enabling early detection and guided interventions such as fetoscopic laser ablation and intrauterine transfusion. Although the use of ionizing modalities like CT and X-ray is limited due to teratogenic risks, they remain essential in maternal life-threatening emergencies. The integration of artificial intelligence (AI) into radiologic imaging promises greater precision, automation, and predictive power in future obstetric care. Keywords: Multiple Pregnancy, Twin to Twin Syndrome, Doppler, Ultrasound. References 1. Kazandi, M. and Turan, V. (2011) “Multipl Pregnancies and Their Complications,” Journal of Turkish Society of Obstetric and Gynecology, 8(1), pp. 21–24. Available at: https://doi.org/10.5505/ tjod.2011.47704. 2. Arrowsmith, S. 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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".