Product Images Losartan Potassium
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Product Label Images
The following 7 images provide visual information about the product associated with Losartan Potassium NDC 71610-009 by Aphena Pharma Solutions - Tennessee, Llc, 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.
71610 0009 30

Losartan Potassium is a medication that comes in the form of 30 tablets containing 50mg of the compound. The label number of this medication is Nock 71610-0009-30, and the product code is WFG LoT 1234. The rest of the text is not readable.*
figure01 - figure01

The text appears to be a graph showing the percentage of patients with the primary endpoint of a study, where the treatments compared are Atenolol and Losartan potassium. There is an "Adjusted Risk Reduction" of 13% with a p-value of 0.021. The x-axis represents the study months, from 0 to 66.*
figure02 - figure02

This is a report on the comparison of the effectiveness of Atenolol and Losartan potassium in reducing the risk of fatal/non-fatal stroke in patients. The text provides a graph showing the study month against the percentage of patients with a fatal/non-fatal stroke. The percentage shown for both drugs is 0, and there is a 25% adjusted risk reduction with a significance level of p=0.00.*
figure03 - figure03

This appears to be a table showing results from a study comparing the effectiveness of Losartan K and Atenolol for Primary Composite Stroke (fatal/non-fatal). The table includes information on overall results, age, gender, race, diabetes and history of CVD. It seems that Losartan K may be favorably effective over Atenolol based on the given results. The data has been adjusted for baseline Framingham risk score and electrocardiographic left ventricular hypertrophy level.*
figure04 - figure04

This appears to be a table or graph with some numerical values, medication names, and a calculation for risk reduction. Without more context or visual information, it is difficult to provide a specific description or interpretation.*
* 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.