During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Background: Osteoarthritis (OA) a common disease of aged population and one of the leading causes of disability. Osteoarthritis affects specific joints more than others in certain ethnic groups. Objective: The aim of this study was to assess the pattern of osteoarthritis among ethnic people in Khagrachari of Bangladesh. Methods: This was a descriptive type of cross sectional community based study. A pretested structured questionnaire was used to collect the data among conveniently selected 200 samples. History of patient, X-ray and physical examination was used to diagnoses osteoarthritis. Data were entered and analyzed by using SPSS software. Verbal consent was taken from community Head as well as respondents before initiation of interview. Results: Maximum was in the age group of 51-60 years. Forty two percent were illiterate. About 30% were housewives and 32% were engaged in agriculture. Among the respondents 58% were from middle income group. Knee was the most affected (85%) organ of osteoarthritis. Most of the respondents (42%) suffered >12 month pain. Almost all of them feel pain during walking long distance. Conclusion: Most prevalent age to develop osteoarthritis was above 50 year. Most frequent affected joint was knee. Hill climbing may be aggravated factor.

Background: Prolonged length of stay (LOS) following targeted temperature management (TTM) administered after cardiac arrest may affect healthcare plans and expenditures. This study identified risk factors for prolonged LOS in patients with cardiac arrest receiving TTM and explored the association between LOS and neurological outcomes after TTM. Methods: The retrospective cohort consisted of 571 non-traumatic cardiac arrest patients aged 18 years or older, treated with cardiopulmonary resuscitation (CPR), had a Glasgow Coma Scale score <8, or were unable to comply with commands after the restoration of spontaneous circulation (ROSC), and received TTM less than 12 hours after ROSC. Prolonged LOS was defined as LOS beyond the 75th quartile of the entire cohort. We analyzed and compared relevant variables and neurological outcomes between the patients with and without prolonged LOS and established prediction models for estimating the risk of prolonged LOS. Results: The patients with in-hospital cardiac arrest had a longer LOS than those with out-of-hospital cardiac arrest (p = 0.0001). Duration of CPR (p = 0.02), underlying heart failure (p = 0.001), chronic obstructive pulmonary disease (p = 0.008), chronic kidney disease (p = 0.026), and post-TTM seizures (p = 0.003) were risk factors for prolonged LOS. LOS was associated with survival to hospital discharge, and patients with the lowest and highest Cerebral Performance Category scores at discharge had a shorter LOS. A logistic regression model based on parameters at discharge achieved an area under the curve of 0.840 to 0.896 for prolonged LOS prediction, indicating the favorable performance of this model in predicting LOS in patients receiving TTM. Conclusions: Our study identified clinically relevant risk factors for prolonged LOS following TTM and developed a prediction model that exhibited adequate predictive performance. The findings of this study broaden our understanding regarding factors associated with hospital stay and can be beneficial while making clinical decisions for patients with cardiac arrest who receive TTM.

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