Product Images Losartan Potassium
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 6 images provide visual information about the product associated with Losartan Potassium NDC 71335-0972 by Bryant Ranch Prepack, 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.
This appears to be a product label for Losartan, a medication used to treat high blood pressure. It contains information on the dosage, storage instructions, and manufacturer details. The label also provides a warning to keep this medication out of reach of children. Additionally, it lists the NDC code and expiration date.*
The text appears to be a chart comparing the effectiveness of two drugs, Atenolol and Losartan Potassium, in reducing the risk of the primary endpoint in patients. The chart shows a 13% adjusted risk reduction for Losartan Potassium compared to Atenolol, with a p-value of 0.021. The rows of numbers at the bottom of the chart indicate the study month.*
This appears to be a table or chart related to the "Primary Endpoint Events" within demographic subgroups. It shows the results for different groups based on age, gender, race, and other factors. However, due to the poor quality of the text, it is difficult to interpret the data accurately.*
The given text shows the results of a clinical trial for a drug called Losttan Potassium compared to a placebo. The top row shows the percentage of patients who experienced a certain event (not specified in the text), with 60% in the Lostan Potassium group and 40% in the placebo group. The next row shows a risk reduction percentage of 16.1% with a statistically significant p-value of 0.022. Therefore, Lostan Potassium was found to be more effective than the placebo in preventing or reducing the occurrence of the specified event.*
* 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.