Evidence map›Paper›PMID 26759181›Full record

ArticleScientific reports2016

Variations in the buccal-lingual alveolar bone thickness of impacted mandibular third molar: our classification and treatment perspectives.

Jing Ge, Jia-Wei Zheng, Chi Yang, Wen-Tao Qian

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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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

22 citing papers in PubMed.

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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.

Jing GeDepartment of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, 200000, China.
Jia-Wei ZhengDepartment of Oral-maxillofacial Head and Neck Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, 200000, China.
Chi YangDepartment of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, 200000, China.
Wen-Tao QianDepartment of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, 200000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Selecting either buccal or lingual approach for the mandibular third molar surgical extraction has been an intense debate for years. The aim of this observational retrospective study was to classify the molar based on the proximity to the external cortical bone, and analyze the position of inferior alveolar canal (IAC) of each type. Cone-beam CT (CBCT) data of 110 deeply impacted mandibular third molars from 91 consecutive patients were analyzed. A new classification based on the mean deduction value (MD) of buccal-lingual alveolar bone thickness was proposed: MD≥1 mm was classified as buccal position, 1 mm>MD>-1 mm was classified as central position, MD≤-1 mm was classified as lingual position. The study samples were distributed as: buccal position (1.8%) in 2 subjects, central position (10.9%) in 12 and lingual position (87.3%) in 96. Ninety-six molars (87.3%) contacted the IAC. The buccal and inferior IAC course were the most common types in impacted third molar, especially in lingually positioned ones. Our study suggested that amongst deeply impacted mandibular third molars, lingual position occupies the largest proportion, followed by the central, and then the buccal type.

Indexed as

AdolescentAdultAgedCone-Beam Computed TomographyFemaleHumansImaging, Three-DimensionalMaleMandibleMiddle AgedMolar, ThirdRetrospective StudiesTooth ExtractionYoung Adult

Identifiers

PMID26759181
PMCPMC4725356

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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.