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BackgroundOrthopedic surgeries are usually associated with excessive blood loss which leads surgeons to overestimate need for blood transfusions and over ordering of blood. The cross matched blood, when not used, leads to the wastage of blood bank resources in terms of time, money and manpower. The objective of this study was to investigate the compliance to previously proposed MSBOS and to provide updated recommendations for all orthopedic procedures.MethodsA retrospective analysis was conducted between 1st June 2015 and 31st May 2016. Patients admitted to the orthopedic surgery service for whom blood products were requested were included. Cross Match/Transfusion (CT) Ratio, Transfusion Index and Transfusion Probability were calculated. Values of < 2.5, > 0.5 and > 30% respectively, were taken as standards. Maximum Surgical Blood Ordering Schedule (MSBOS) was proposed based upon these calculations using Mead’s criteria.ResultsSix hundred and ninety-nine patients were sampled after implementing exclusion criteria. The overall CT ratio was 4.87, transfusion index was 0.55 and transfusion probability was 25%. A compliance rate of 24.6% was observed with the reference CT ratio of 2.5. Highest CT ratio was calculated for arthroscopic procedures while tumor resection had the lowest ratio. Age, procedure performed, ASA status and use of tourniquet were found to be significantly associated with CT ratio being greater or less than 2.5.ConclusionResults showed significant wastage of blood products and non-compliance with blood ordering guidelines. Hence there is need for large scale prospective studies to establish MSBOS and ensure its compliance.

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Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Background: We aimed to examine the role played by the COVID-19 infection in patients' death and to determine the proportion of patients for whom it was a major contributor to death. Methods:We included patients ≥50 years old who were hospitalized with COVID-19 infection and died between March 1, 2020 and September 30, 2020 in a tertiary medical center. We considered COVID-19 infection to be a major cause for death if the patient had well-controlled medical conditions and death was improbable without coronavirus infection, and a minor cause for death if the patient had serious illnesses and had an indication for palliative care.Results: Among 243 patients, median age was 80 (interquartile intervals: 72-86) and 40% were female. One in two had moderate or severe frailty and 41% had dementia.Nearly 60% of the patients were classified as having advanced, serious illnesses present prior to the hospitalization, with death being expected within 12 months, and among this group 39% were full code at admission. In the remaining 40% of patients, deaths were classified as unexpected based on patients' prior conditions, suggesting that COVID-19 infection complications were the primary contributor to death.Conclusions: For slightly less than half (40%) of patients who died of complications of COVID-19, death was an unexpected event. Among the 60% of patients for whom death was not a surprise, our findings identify opportunities to improve endof-life discussions and implement shared decision-making in high-risk patients early on or prior to hospitalization. BACKGROUNDAs of October 2021, more than 660,000 people in the United States have died from complications related to COVID-19. 1 In one metaanalysis, global mortality for hospitalized patients was estimated at 17% in patients not admitted to the intensive care unit and 40% in studies of critically ill patients. 2 Factors associated with mortality include older age, chronic medical conditions (e.g., diabetes mellitus, chronic lung diseases, obesity or hypertension), 3 and frailty. 4 Although it is likely that many patients who die with COVID-19 were low-risk prior to contracting the illness, the number that come from lower risk groups is unknown. In fact, some lay outlets and even scientific papers have argued that almost no low-risk patients die of COVID. 5,6