A cut-off value of third molar maturity index for indicating a minimum age of criminal responsibility: Older or younger than 16 years?

Providing appropriate legal mechanisms, that evaluate the progression of development from the age of childhood innocence to maturity and full responsibility, considered one of the difficult areas of criminal justice policy. The minimum age of criminal responsibility (MACR) in children varies among countries and differs widely owing to history and culture. Due to rising and brutality of criminal offenses, particularly by juveniles, few countries have lowered the minimum age of criminal responsibility, and many have considered/considering to do the same. India is one such country in which is under the proposal of lowering the age of criminal responsibility to 16 years. As there is lack of useful age assessment methods, that can indicate whether if the individual in question is younger (<16 years) or older than MACR (≥16 years), the present study was undertaken to derive a specific cut-off value of the third molar maturity index (I3M) for this purpose. The sample consisted of 1078 orthopantomograms (OPTs) from Andhra Pradesh, India, aged between 11 and 20 years (45.4% males and 54.6% females). The reproducibility of I3M was calculated by intra-class correlation coefficients, which showed an intra-rater and inter-rater agreement of 0.912 and 0.891, respectively. The sample was divided into training dataset (819 OPTs), to test I3M and gender as independent variables and MACR as a dependent variable by logistic regression analysis, and test dataset (259 OPTs). A receiver operating curve (ROC) analysis was performed to evaluate the specific cut-off value of I3M for predicting MACR status. A logistic regression analysis showed that gender was not statistically significant for predicting MACR status while ROC analysis indicated a cut-off value of I3M = 0.293 as best for predicting MACR status. The performance of derived cut-off value was tested in a test data set. The sensitivity of the test was 90.6% and 90%, while specificity was 86% and 87.1% in males and females, respectively. The proportion of correctly classified individuals was 88.0% and 88.7% in males and females, respectively. The estimated Bayes post-test probability in males was 87.2% and while in females it was 88.3%. The results highlight the contribution of the derived cut-off value of I3M for discriminating individuals around MACR and should be evaluated in other populations.