Evidence map›Paper›PMID 39472249›Full record

ArticleInternational dental journal2025

A Prediction Model for External Root Resorption of the Second Molars Associated With Third Molars.

Zhengwei Kou, Wuyang Zhang, Chen Li, Yu Zhang, Zijian Song, Yuzhen Zou, Haijing Wang, Zhenghua Liu, Bahetibieke Huerman, Tiange Deng and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
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

13 authors.

Zhengwei KouChongqing Key Laboratory of Oral Diseases and Biomedical Sciences, College of Stomatology, Chongqing Medical University, Chongqing, China; People's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Wuyang ZhangDepartment of Oral and Maxillofacial Surgery, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Chen LiDepartment of Health Statistics, School of Preventive Medicine, Fourth Military Medical University, Xi'an, China.
Yu ZhangDepartment of Oral and Maxillofacial Surgery, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Zijian SongDepartment of Oral and Maxillofacial Surgery, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Yuzhen ZouPeople's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Haijing WangPeople's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Zhenghua LiuPeople's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Bahetibieke HuermanPeople's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Tiange DengDepartment of Oral and Maxillofacial Surgery, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Kaijin HuSchool of Stomatology, Xi'an Medical University, Stomatological Hospital of the Third Affiliated Hospital of Xi'an Medical University, Xi'an, China.
Yang XueDepartment of Oral and Maxillofacial Surgery, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, School of Stomatology, The Fourth Military Medical University, Xi'an, China. Electronic address: xueyangfmmu@163.com.
Ping JiChongqing Key Laboratory of Oral Diseases and Biomedical Sciences, College of Stomatology, Chongqing Medical University, Chongqing, China. Electronic address: jiping@hospital.cqmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study is to investigate risk factors for external root resorption (ERR) of second molars (M2) associated with impacted third molars (M3), and to develop a prediction model that can offer dentists a reliable and efficient tool for predicting the likelihood of ERR.

methodsA total of 798 patients with 2156 impacted third molars were collected from three centres between 1 December 2018 and 15 December 2018. ERR was identified by cone beam computed tomography examinations. The effects of different risk factors on the presence/absence of ERR and its severity were analysed using Chi-square or Fisher test. Multivariate logistic regressive analysis with stepwise variable selection methods was performed to identify factors which were significant predictors for ERR and its severity. Subsequently, a prediction model was developed, and the model performance was validated internally and externally.

resultsThe overall incidence of ERR of second molars was 16.05%. The prediction model was established using six factors including position (upper/lower jaw), impact type, impact depth (PG: A-B-C), contact position, root number of M3, and age. In terms of internal validation, the prediction model demonstrated satisfactory performance, achieving an area under curve of 0.961 and a prediction accuracy of 0.907. As for external validation, the area under curve remained high at 0.953, with a prediction accuracy of 0.892.

conclusionA risk prediction model for ERR was established in the present study. Position (upper or lower jaw), impact type, impact depth (PG: A-B-C), contact position, root number of M3, and age were identified as influencing variables which were significant predictors in the development of this predictive model. The prediction model showed great discrimination and calibration. CLINICAL RELEVANCE: This prediction model has the potential to aid dentists and patients in making clinical decisions regarding the necessity of M3 extraction.

Indexed as

MolarMolar, ThirdRoot ResorptionTooth, ImpactedAdolescentAdultCone-Beam Computed TomographyFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsYoung AdultExternal root resorptionPrediction modelRisk factorThird molar

Identifiers

PMID39472249
PMCPMC11806329

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.