The application of <i>in utero</i> magnetic resonance imaging in the study of the metabolic and cardiovascular consequences of the developmental origins of health and disease
Bibliographic record
Abstract
Observing fetal development in utero is vital to further the understanding of later-life diseases. Magnetic resonance imaging (MRI) offers a tool for obtaining a wealth of information about fetal growth, development, and programming not previously available using other methods. This review provides an overview of MRI techniques used to investigate the metabolic and cardiovascular consequences of the developmental origins of health and disease (DOHaD) hypothesis. These methods add to the understanding of the developing fetus by examining fetal growth and organ development, adipose tissue and body composition, fetal oximetry, placental microstructure, diffusion, perfusion, flow, and metabolism. MRI assessment of fetal growth, organ development, metabolism, and the amount of fetal adipose tissue could give early indicators of abnormal fetal development. Noninvasive fetal oximetry can accurately measure placental and fetal oxygenation, which improves current knowledge on placental function. Additionally, measuring deficiencies in the placenta's transport of nutrients and oxygen is critical for optimizing treatment. Overall, the detailed structural and functional information provided by MRI is valuable in guiding future investigations of DOHaD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".