ReviewOrthodontics & craniofacial research2009
Genetic studies of craniofacial anomalies: clinical implications and applications.
Review in Orthodontics & craniofacial research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 90 citations in OpenAlex.
- Craniofacial features and pathogenic variants in 1,252 children with neurodevelopmental disorders.BMC pediatrics · 2026Article
- The Power of Partnership: Exploring Patterns of Interdisciplinary Research Collaboration in Plastic Surgery.Plastic and reconstructive surgery. Global open · 2026Article
- Unlocking 3D baby face photogrammetry: Multi-view BabyMorph reconstruction from uncalibrated photographs.Expert systems with applications · 2026Article
- Workflow analysis and evaluation of a next-generation phenotyping tool: A qualitative study of Face2Gene.European journal of human genetics : EJHG · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.medRxiv : the preprint server for health sciences · 2024Article
- The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.Functional & integrative genomics · 2024Review
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.Research square · 2024Article
- An interactive atlas of three-dimensional syndromic facial morphology.American journal of human genetics · 2024Article
- Comparing 2D and 3D representations for face-based genetic syndrome diagnosis.European journal of human genetics : EJHG · 2023Article
- An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound.Bioengineering (Basel, Switzerland) · 2023Article
- A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound.Biomedicines · 2023Article
- The future role of facial image analysis in ACMG classification guidelines.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2023Article
- 3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema.PLOS digital health · 2023Article
- The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases.International journal of environmental research and public health · 2023Article
- Classification of multiple emotional states from facial expressions in head-fixed mice using a deep learning-based image analysis.PloS one · 2023Article
- Multidisciplinary management of Papillon-Lefevre syndrome as a result of consanguineous marriage.BMJ case reports · 2022Article
- Parents' perspectives on the use of children's facial images for research and diagnosis: a survey.Journal of community genetics · 2022Article
- Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models.Artificial intelligence in medicine · 2022Article
- A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis.IEEE journal of biomedical and health informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
The objective of the study was to overview the role of genetic research in fostering translational studies of craniofacial diseases of dental interest. Background information is presented to illustrate influences affecting genetic research studies of Mendelian diseases. Genetic studies of amelogenesis imperfecta, dentinogenesis imperfecta, hereditary gingival fibromatosis and Papillon Lefèvre syndrome are reviewed. Findings are presented to illustrate how translational applications of clinical and basic research may improve clinical care. Clinical and basic science research has identified specific genes and mutations etiologically responsible for amelogenesis imperfecta, dentinogenesis imperfecta, hereditary gingival fibromatosis and Papillon Lefèvre syndrome. These findings are enabling researchers to understand how specific genetic alterations perturb normal growth and development of dental tissues. Identification of the genetic basis of these conditions is enabling clinicians and researchers to more fully understand the etiology and clinical consequences of these diseases of dental importance. Findings from genetic studies of dental diseases provide a basis for diagnostic genetic testing and development of therapeutic intervention strategies directed at the underlying disease etiology. These studies are advancing our understanding of the development of dental tissues in health and disease. The dental community must consider how to incorporate these developments into effective disease prevention paradigms to facilitate the diagnosis and treatment of individuals with genetic diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.