The effect of exercise therapy on knee osteoarthritis: A randomized clinical trial

Background: Knee osteoarthritis (OA) is the most common musculoskeletal disease among old individuals which affects ability for sitting on the chair, standing, walking and climbing stairs. Our objective was to investigate the short and long-term effects of the most simple and the least expensive exercise protocols in combination to conventional conservative therapy for knee OA. Methods: It was a single blind RCT study with a 12-months follow-up. Totally, 56 patients with knee OA were assigned into 2 random groups. The patients in exercise group received exercise for knee muscles in combination with non-steroid anti-inflammatory drugs (NSAIDs) and 10 sessions acupuncture and physiotherapy modalities. Non-exercise group received similar treatments except exercise program. The changes in patients' pain and functional status were evaluated by visual analog scale (VAS), knee and osteoarthritis outcome score (KOOS) questionnaire and functional tests (4 steps, 5 sit up, and 6 min walk test) before and after treatment (1 and 3 months after intervention), and 1 year later at the follow-up. Results: The results showed that the patients with knee OA in exercise group had significant improvement in pain, disability, walking, stair climbing, and sit up speed after treatment at first and second follow-up when compared with their initial status and when compared with non-exercise group. At third follow up (1 year later) there was significant difference between groups in VAS and in three items of KOOS questionnaire in functional status. Conclusion: Non aerobic exercises for muscles around knee can augment the effect of other therapeutic interventions like medical therapy, acupuncture, and modalities for knee OA

A web-based algorithm to rapidly classify seizures for the purpose of drug selection

Objective: To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making. Methods: Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting. Results: A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%–87.8%), with an agreement coefficient (AC1) of.82 (95% confidence interval =.77–.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale). Significance: The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults