Evidence map›Paper›PMID 35499533›Full record

ArticleThe Journal of forensic odonto-stomatology2022

Application of third molar maturity index for Indonesia minimum legal age of marriage: a pilot study.

R M Boedi, H Ermanto, T H Skripsa, Y B Prabowo

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Article in The Journal of forensic odonto-stomatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Bibliometric analysis of research on dental age estimation.The Journal of forensic odonto-stomatology · 2023
    Article
4 · The record

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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.

R M BoediDepartment of Dentistry, Universitas Diponegoro, Tembalang, Semarang, Indonesia.
H ErmantoDepartment of Dentistry, Universitas Diponegoro, Tembalang, Semarang, Indonesia.
T H SkripsaDepartment of Dentistry, Universitas Diponegoro, Tembalang, Semarang, Indonesia.
Y B PrabowoDepartment of Dentistry, Universitas Diponegoro, Tembalang, Semarang, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IBackground: Child marriage is recognised as a harmful practice. Recently, Indonesia enacted a new law to raise the minimum age of marriage from 16 to 19 years old for women, creating no minimum age difference between males and females. However, this improvement may be detrimental for individuals in remote areas with no legal documentation and the common practices of age falsification to reach the minimum age of marriage. Therefore, implementing an age estimation technique for juveniles is mandatory to reduce the risk of child marriage.

methodsthis study used the third molar maturity index (I3M) to distinguish an individual under or over 19 years old. I3M values from 222 digital OPGs aged between 15 to 23.99 years were calculated. The sample was randomly assigned as a training dataset (n = 156) and testing dataset (n = 66). The logistic regression model was created using a 5-fold cross-validation method, and the Youden's Index Value was used to establish the I3M cut-off value.

resultsthe logistic regression model showed significance in both sex and I3M value for predicting the probability of minimum age of marriage. I3M cut-off values of 0.08 and 0.09 for males and females, respectively, were taken. The accuracy of this test was 80% for both sexes in the testing dataset.

conclusionsthe outcome of this pilot study showed a promising result of using I3M as a dental age estimation method to determine whether an individual is over or under 19 years old to comply with the newly enacted legal age of marriage in Indonesia. Future research should be carried out using a balanced age cohort for each sex and a more extensive training sample size to investigate the influence of sex in the cut-off value calculation.

Indexed as

Age Determination by TeethMolar, ThirdAdolescentAdultChildFemaleHumansIndonesiaMaleMarriagePilot ProjectsYoung Adult

Identifiers

PMID35499533
PMCPMC10228189

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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.