METHODS: A prospective 1-year study was conducted in rheumatology clinics of tertiary care hospitals of Karachi, Pakistan. Cost-of-illness methodology was used and all patient data related to costs of rheumatologist visits, physical therapy sessions, medications, assistive devices and laboratory investigations were obtained directly in printed hardcopies from patient electronic databases using their medical record numbers. Transportation cost was calculated from patient-reported log books. Data were analyzed through IBM SPSS version 23. Patients were asked to sign a written consent and the study was ethically approved.
RESULTS: The mean age of patients (N = 358) was 48 years. Most patients (73.7%) were female, married (86%) and had basic education (71.8%). Average cost of rheumatologist visits was PKR 11 510.61 (USD: 72.05) while it was PKR 66 947.37 (USD: 419.07) for physical therapy sessions. On average, medicines and medical devices costs were estimated at PKR 10 104.23 (USD: 63.25) and PKR 7848.48 (USD: 49.13) respectively. Cost attributed to diagnostic and laboratory charges was PKR 1962.12 (USD: 12.28) and travel expense was PKR 6541 (USD: 40.95). The direct expenditure associated with managing RA was PKR 37 558 (USD: 235.1). All costs were reported per annum.
CONCLUSION: Patient with RA in Pakistan pay a considerable amount of their income for managing their condition. Most patients have no provision for insurance which is a need considering the nature of the disease and associated productivity loss that would significantly lower income as the disease progresses.
AIM: To conduct an umbrella review to determine whether there is an association between diabetes and the outcome of root canal treatment.
DATA SOURCE: The protocol of the review was developed and registered in the PROSPERO database (CRD42019141684). Four electronic databases (PubMed, EBSCHOhost, Cochrane and Scopus databases) were used to perform a literature search until July 2019.
STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: Systematic reviews with or without meta-analyses published in English assessing any outcomes of root canal treatment comparing diabetic and nondiabetic patients were included. Two reviewers were involved independently in study selection, data extraction and appraising the reviews that were included. Disagreements were resolved with the help of a third reviewer.
STUDY APPRAISAL AND SYNTHESIS METHODS: The quality of the reviews was assessed using the AMSTAR tool (A measurement tool to assess systematic reviews), with 11 items. Each AMSTAR item was given a score of 1 if the criterion was met, or 0 if the criterion was not met or the information was unclear.
RESULTS: Four systematic reviews were included. The AMSTAR score for the reviews ranged from 5 to 7, out of a maximum score of 11, and all the systematic reviews were classified as 'medium' quality.
LIMITATIONS: Only two systematic reviews included a meta-analysis. Only systematic reviews published in English were included.
CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Diabetes mellitus is associated with the outcome of root canal treatment and can be considered as a preoperative prognostic factor.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.