Fetal arthrogryposis: Challenges and perspectives for prenatal detection and management
Bibliographic record
Abstract
Antenatal identification of fetuses with multiple congenital contractures or arthrogryposis multiplex congenita (AMC) may be challenging. The first clinical sign is often reduced fetal movement and/or contractures, as seen on prenatal ultrasounds. This can be apparent at any point, from early to late pregnancy, may range from mild to severe involvement, with or without associated other structural anomalies. Possible etiologies and their prognosis need to be interpreted with respect to developmental timing. The etiology of AMC is highly heterogeneous and making the specific diagnosis will guide prognosis, counseling and prenatal and perinatal management. Current ultrasound practice identifies only approximately 25% of individuals with arthrogryposis prenatally before 24 weeks of pregnancy in a general obstetrics care population. There are currently no studies and guidelines that address the question of when and how to assess for fetal contractures and movements during pregnancy. The failure to identify fetuses with arthrogryposis before 24 weeks of pregnancy means that physicians and families are denied reproductive options and interventions that may improve outcome. We review current practice and recommend adjusting the current prenatal imaging and genetic diagnostic strategies to achieve early prenatal detection and etiologic diagnosis. We suggest exploring options for in utero therapy to increase fetal movement for ongoing pregnancies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".