Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.
METHODS: We analysed incident HIV diagnoses from 2015-2018 and mortality trends from 2016-2018 for three age groups: 1) 15-24 years; 2) 25-49 years; and 3) ≥50 years. AIDS was defined as CD4<200cells/mL. Mortality was defined as deaths per 1000 patients newly diagnosed with HIV within the same calendar year. Mortality rates were calculated for 2016, 2017, and 2018, compared to age-matched general population rates, and all-cause standardized mortality ratios (SMRs) were calculated.
RESULTS: From 2015-2018, the proportion of OPWH annually diagnosed with HIV increased from 11.2% to 14.9% (p<0.01). At the time of diagnosis, OPWH were also significantly (p<0.01) more likely to have AIDS (43.8%) than those aged 25-49 years (29.5%) and 15-24 years (13.3%). Newly diagnosed OPWH had the same-year mortality ranging from 3 to 8 times higher than age-matched groups in the Ukrainian general population.
CONCLUSIONS: These findings suggest a reassessment of HIV testing, prevention and treatment strategies in Ukraine is needed to bring OPWH into focus. OPWH are more likely to present with late-stage HIV and have higher mortality rates. Re-designing testing practices is especially crucial since OPWH are absent from targeted testing programs and are increasingly diagnosed as they present with AIDS-defining symptoms. New strategies for linkage and treatment programs should reflect the distinct needs of this target population.