Bibliographic record
Abstract
Fetal magnetic resonance imaging (MRI) is used with increasing frequency as a complementary imaging modality to ultrasound (US) in prenatal diagnosis. Fetal MRI displays the fetal, uterine, and extrauterine anatomy in ways that allow confirmation of normal anatomy and the diagnosis of pathological entities that were formerly very difficult to detect prenatally. Comparison of US views with standard orthogonal plane MR images reinforces the understanding of fetal anatomy as visualized with US. Technological advances in US equipment have allowed the recent description of subtle fetal anatomical structures. Similarly, knowledge of the MRI appearances of pathological conditions has opened opportunities for the sonographic diagnosis of entities such as brainstem malformations and alterations in the normal transient laminar pattern that occur during development of the fetal cerebrum. Fetal MRI can confirm suspicious US findings and thus add confidence in a particular prenatal diagnosis before performing invasive and interventional procedures. Specific MRI sequences can be used to add information about the chemical composition of fetal structures, such as fat, blood, and meconium. Dynamic MRI sequences have increased understanding of gestational age-dependent behavior, and assist the sonographer in assessment of fetal structural anomalies that cause abnormal movement and behavior. The technological ability of US to demonstrate very small structures complements the lower resolution of fetal MR images, whereas the ability of MR to visualize the whole fetus improves the limited views necessitated by US. Therefore, both US and fetal MRI have complementary strengths and weaknesses that can be used to full advantage in prenatal diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.023 |
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".