Failure to identify antenatal multiple congenital contractures and fetal akinesia – proposal of guidelines to improve diagnosis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to assess the rate of prenatal detection of multiple congenital contractures, to identify reasons for the failure of prenatal diagnosis and to propose the first guidelines to improve prenatal diagnosis. METHOD: We evaluated records on 107 individuals recognized at birth to have Amyoplasia. We reviewed the literature on the onset and development of fetal activity, antenatal clinical signs in fetal movement disorders, prenatal studies of fetal movement and contractures by ultrasound and magnetic resonance imaging (MRI) and existing guidelines. RESULT: In 73.8%, the diagnosis was missed prenatally. Correct diagnosis was achieved by the identification of bilateral clubfeet on ultrasound or because mothers perceived reduced fetal movement. Ultrasound would be able to visualize contractures, joint positioning, the quality of fetal movements, lung size, muscle tissue, and bone growth in the first or early second trimester. MRI results are promising. Guidelines for assessing early fetal movement do not exist. CONCLUSION: Prenatal detection rate of multiple congenital contractures is appalling. Failure of diagnosis precludes further etiologic and diagnostic workup and deprives families of making informed pregnancy choices. Standards for prenatal diagnosis are lacking, but on the basis of current knowledge and expert opinion, we propose the first guidelines for a prenatal diagnostic strategy, discuss future directions and the need for multicentric studies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".