Bibliographic record
Abstract
Abstract Fetal growth restriction (FGR) has a prevalence of about 10% worldwide and is associated with an increased risk of perinatal mortality and morbidity. FGR is commonly caused by placental insufficiency and can begin early (<32 weeks) or in late (≥32 weeks) gestational age. A false positive antenatal diagnosis may lead to unnecessary monitoring and interventions, as well as cause maternal anxiety. Whereas a false negative diagnosis exposes the fetus to an increased risk of stillbirth and renders the pregnancy ineligible from the appropriate care and potential treatments. The clinical management of FGR pregnancies faces a complex challenge of deciding on the optimal timing of delivery as currently the main solution is to deliver the baby early, but iatrogenic preterm delivery of infants is associated with adverse short- and long-term outcomes. Early and accurate diagnosis of FGR could aid in better stratification of clinical management, and the development and implementation of treatment options, ultimately benefiting clinical care and potentially improving both short- and long-term health outcomes. The aim of this review is to present the new insights on biomarkers of placenta insufficiency, including their current and potential value of biomarkers in the prediction and prevention for FGR, and highlight the association between biomarkers and adverse outcomes in utero to explore the specific mechanism of impaired fetal growth that establish the basis for disease later in life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".