Product Images Losartan Potassium
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Product Label Images
The following 6 images provide visual information about the product associated with Losartan Potassium NDC 51655-791 by Northwind Pharmaceuticals, such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.
Losartan Potassium is a prescription drug available as 100mg tablets provided in a container of 30 tablets. The dosage and storage instructions should be followed as provided in the package insert. It should be stored at room temperature, between 20°C to 25°C (68°F-77°F) with short deviations allowed to 15°C-30°C (59°F-86°F). The medication should be kept out of the reach of children, in its original container, and tightly closed for protection from moisture. The repackaged version of the drug is produced by Northwind Pharmaceuticals and contains fim-coated Losartan potassium USP 100mg manufactured by Aurobindo Pharma USA, Inc. The lot and expiration dates are 0000000000 and 00/00/0000, respectively.*
This appears to be a data plot or table from a study comparing the medications Atenolol and Losartan Potassium. The plot shows an "Adjusted Risk Reduction" of 13% with a corresponding statistical significance indicated by "p=" and some numbers. The x-axis appears to represent study months from 6 to 66. Without more context, it is not possible to determine the specific details or conclusions of the study.*
The text suggests the results of a clinical trial that compared the effectiveness of Losttan Potassium versus a placebo in reducing the risk of an unmentioned event. The trial involved 100 patients, 60 of which experienced the event during the trial and received Losttan Potassium, while the remaining 40 received the placebo. The percent reduction in risk associated with Losttan Potassium is 16.1%, which was statistically significant with a p-value of 0.022.*
* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.