DIAGNÓSTICO TEMPRANO DE DIFERENCIAS CONGÉNITAS CRANEOFACIALES: UNA REVISIÓN SISTEMÁTICA DE LA EVIDENCIA ACTUAL
Bibliographic record
Abstract
Introduction: Craniofacial congenital differences (CCDs) impact vital functions and have psychosocial implications. Early diagnosis is crucial to optimize prognosis and quality of life. Objective: To analyze the current evidence on the early diagnosis of CCDs, identifying effective methodologies and gaps in knowledge. Materials and Methods: This systematic review was conducted following the PRISMA 2020 guidelines. The literature search included 25 studies published between January 2020 and June 2025, covering a variety of designs, such as cohort studies, diagnostic accuracy studies, and randomized controlled trials (RCTs). The consulted databases were PubMed, Scopus, Web of Science, ScienceDirect, LILACS, and Cochrane Library. Study selection and data extraction were performed independently by two reviewers to ensure the reliability of the information. The methodological quality and risk of bias of the included studies were assessed using specific tools: QUADAS-2 for diagnostic accuracy studies, RoB 2.0 for RCTs, and the Newcastle-Ottawa Scale (NOS) for cohort studies. Finally, the overall certainty of evidence was determined using the GRADE rating system. Results: Prenatal ultrasound showed 75% sensitivity and 98% specificity for cleft lip and palate. Fetal MRI achieved 90% sensitivity and 97% specificity for complex CCDs. Prenatal methods allowed for earlier diagnosis (mean 24 weeks of gestation) and facilitated clinical planning. Evidence of the long-term impact on functional/aesthetic prognosis was limited (very low GRADE). Adverse effects were minimal Conclusions: Prenatal ultrasound and fetal MRI are useful for the early diagnosis of CCDs, improving clinical management. Rigorous research is needed, especially longitudinal studies, to evaluate long-term prognosis and address global disparities.
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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 | 0.063 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".