ArticleScientific reports2017
Bayesian Networks Analysis of Malocclusion Data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- AI in orthodontic treatment planning: a systematic review comparing learning approaches.BMC oral health · 2026Pooled it
- A study of correlations between cephalometric measurements in Koreans with normal occlusion by network analysis.Scientific reports · 2024Article
- Antimicrobial Susceptibility in Respiratory Pathogens and Farm and Animal Variables in Weaned California Dairy Heifers: Logistic Regression and Bayesian Network Analyses.Antibiotics (Basel, Switzerland) · 2024Article
- Identifying Associations in Minimum Inhibitory Concentration Values ofFrontiers in veterinary science · 2022Article
- Bagged random causal networks for interventional queries on observational biomedical datasets.Journal of biomedical informatics · 2021Article
- Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study.Journal of medical Internet research · 2020Article
- Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care.Sensors (Basel, Switzerland) · 2020Article
- A Bayesian Network Analysis of the Diagnostic Process and Its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study.JMIR medical informatics · 2019Article
- Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification.Scientific reports · 2019Article
- Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.
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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.