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  1. Fulltext Assessment of the success rate of endodontically treated patients attending outpatient polyclinic
    Mustafa NS, Kashmoola MA, Majeed KRA, Qader OAJA
    Eur J Dent, 2018 10 30;12(4):540-545.
    PMID: 30369800 DOI: 10.4103/ejd.ejd_377_17
    Objectives: The aim of this study is to determine the success rate of the endodontically treated teeth in patients attending the Polyclinic, Kulliyyah of Dentistry, International Islamic University Malaysia (IIUM), from 2012 to 2015.

    Materials and Methods: A retrospective study involved endodontically treated teeth of patients attending the Polyclinic, Kulliyyah of Dentistry, IIUM, from 2012 to 2015. Clinical and radiographic data were recorded and classified as successful or failed, and further analyzed by Fisher's exact test to measure the correlation between the variables using SPSS software version 16.0. Kappa test was used to measure the overall relationship between clinical and radiographic findings.

    Results: A total of sixty teeth were evaluated clinically and radiographically, the overall success rate was 85% (n = 51). Correlation between the variables showed nonsignificant (P > 0.05) in the success rate among age, gender, and race, upper and lower arches and between anterior and posterior teeth at the time of treatment. At postendodontic fixed restorations, the variables showed statistically significant relationship with the success rate (P < 0.05).

    Conclusions: Patients with no signs and symptoms and with no radiographical changes at the the time of clinical examination, showed the highest percentage of success rate (85%) of postendodontic fixed restorations. Age, gender, and race have no significant relations with the success rate of endodontically treated teeth.

  2. Fulltext Identification of dental related ChatGPT generated abstracts by senior and young academicians versus artificial intelligence detectors and a similarity detector
    Al-Rawas M, Qader OAJA, Othman NH, Ismail NH, Mamat R, Halim MS, et al.
    Sci Rep, 2025 Apr 02;15(1):11275.
    PMID: 40175423 DOI: 10.1038/s41598-025-95387-y
    Several researchers have investigated the consequences of using ChatGPT in the education industry. Their findings raised doubts regarding the probable effects that ChatGPT may have on the academia. As such, the present study aimed to assess the ability of three methods, namely: (1) academicians (senior and young), (2) three AI detectors (GPT-2 output detector, Writefull GPT detector, and GPTZero) and (3) one plagiarism detector, to differentiate between human- and ChatGPT-written abstracts. A total of 160 abstracts were assessed by those three methods. Two senior and two young academicians used a newly developed rubric to assess the type and quality of 80 human-written and 80 ChatGPT-written abstracts. The results were statistically analysed using crosstabulation and chi-square analysis. Bivariate correlation and accuracy of the methods were assessed. The findings demonstrated that all the three methods made a different variety of incorrect assumptions. The level of the academician experience may play a role in the detection ability with senior academician 1 demonstrating superior accuracy. GPTZero AI and similarity detectors were very good at accurately identifying the abstracts origin. In terms of abstract type, every variable positively correlated, except in the case of similarity detectors (p 
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