Diagnostic Methods for the Prenatal Detection of Cleft Lip and Palate: A Systematic Review
Bibliographic record
Abstract
Background: Accurate prenatal diagnosis of cleft lip and palate is essential to discuss severity prediction, perform appropriate parental counseling, and, at last, establish long-term treatment planning. The aim of this systematic review was to analyze the accuracy of various imaging techniques for the prenatal diagnosis of cleft lip and palate, assess the pregnancy phase for orofacial clefts diagnosis, and study the different cleft types in terms of diagnostic methods, timing, and predictability. Methods: A search of the PubMed, EMBASE, Scopus, and Web of Science databases was conducted to identify potentially relevant studies published until January 2024. The quality of the selected articles was assessed using the Newcastle–Ottawa scale for methodological quality assessment of cohort studies and the QUADAS-2 scale for diagnostic test studies. Results: A total of 18 studies met the eligibility criteria and were included in the review. The findings of this review indicate that the majority of studies showed improved diagnostic accuracy when supplementary techniques, such as 3D ultrasound or magnetic resonance imaging, were added to 2D ultrasound. Conclusions: The implementation of magnetic resonance imaging as a standard procedure could significantly improve the precision of diagnosing cleft lip and palate. Therefore, the diagnostic technique used will play a crucial role in the accuracy of the diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".