Evidence map›Paper›PMID 39799343›Full record

ArticleBMC oral health2025

Bayesian network for predicting mandibular third molar extraction difficulty.

Tian Meng, Zhiyong Zhang, Xiao Zhang, Chao Zhang

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07461324 (Systematic Risk Assessment Prior to Oral Surgery), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT07461324 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Systematic Risk Assessment Prior to Oral Surgery

Typeobservational_patient_registrySponsorUniversity of CopenhagenRan2026 to 2030Enrolled3,000ConditionsPostoperative Infections, Mandibular Nerve Injuries, Alveolar Osteitis, Complication
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tian MengFirst Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China. ddsmengtian@163.com.
Zhiyong ZhangFirst Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Xiao ZhangFirst Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Chao ZhangSub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.

methodsRelevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network. First, the qualitative mechanism knowledge, including the effect of risk factors on the extraction difficulty and the causal relationships between risk factors, was analysed to establish the framework of the Bayesian network. Then, the quantitative knowledge, including the occurrence probability of the parent nodes and the conditional probability table of the nodes with causal relationships, was given by the surgeon experience and calculated using the Dempster-Shafer evidence theory. According to the framework and likelihoods and relationships of risk factors, the Bayesian network model was established.

resultsThis Bayesian network model analysed the weight by sensitivity of each risk factor and expressed the interaction relationship among risk factors as well as the effect of risk factors on extraction difficulty quantitatively. This Bayesian network model showed quantitative analysis results for extraction difficulty and key risk factors. The Bayesian network model revealed that the relationship to the inferior alveolar nerve, surgeon experience and patient anxiety were the most important risk factors for extraction difficulty. By integrating these patient-specific risk factors across the entire surgical process, this model could be used during preoperative planning to identify high-risk cases and to optimize resource allocation; during intraoperative management to tailor surgical techniques; and during postoperative follow-up to establish targeted follow-up protocols for high-risk patients. Moreover, this Bayesian network model can flexibly improve inclusion factors and conditional probabilities with the development of relevant research and expert opinions, as well as change states and probabilities of relevant nodes based on actual clinical conditions.

conclusionsA model for predicting the difficulty of mandibular third molar extraction was established based on a Bayesian network.

Indexed as

MandibleMolar, ThirdTooth ExtractionBayes TheoremHumansRisk FactorsArtificial intelligenceClinical decision-makingIntraoperative complicationsPostoperative complicationsPreoperative periodRisk assessmentTooth extraction

Identifiers

PMID39799343
PMCPMC11725194

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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.