A transparent and evidence-based priority-setting process promotes the optimal use of resources to improve health outcomes. Decision-makers and funders have begun to increasingly engage representatives of patients and healthcare consumers to ensure that research becomes more relevant. However, disadvantaged groups and their needs may not be integrated into the priority-setting process since they do not have a "political voice" or are unable to organise into interest groups. Equitable priority-setting methods need to balance patient needs, values, experiences with population-level issues and issues related to the health system.

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Abstract. The authors of previous studies on the role of photocoagulation for central serous chorioretinopathy (CSR) have based their deductions on the premise that a Snellen visual acuity of 6/6 is the end point of recovery. It is now known that patients with a visual acuity of 6/6 may have defective contrast sensitivity indicative of a visual function deficit. The present study was a prospective, controlled, and randomized evaluation of patients suffering from their first attack of CSR, in which contrast sensitivity was used to determine the effectiveness of argon laser photocoagulation as compared with more conservative treatment. Although long-term studies are necessary, the results of this study showed that while photocoagulation increases the rate of recovery for visual acuity, it is also linked with significant loss and slower recovery of contrast sensitivity. [Ophthalmic Surg Lasers 1997;28:693-697.]

The global problem at the moment is COVID-19 caused by corona-virus which led to the worldwide lockdown. However there are still people who are not taking proper precautions and maintaining social-distancing while going out for emergency situations. To ensure this the government is trying to monitor the actions of each and every citizen. It is not realistically possible to monitor the actions of each and every citizen. This problem can be possibly solved by the use of machine learning. In this paper we have presented face detection based technique to combat with COVID-19. At first this technique captures the image and detects the face using viola zones method. Then the detected face images are classified to detect the mask. It actually detects whether every person have used mask or not. If there present anyone without mask then the system starts alarm to inform it to the responsible authority and to alert the public. Here Principal Component Analysis (PCA) has been used to extract the feature from the detected face images and for the detection of mask K-Nearest Neighbor (KNN) classifier has been used.