Product Images Carvedilol

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 6 images provide visual information about the product associated with Carvedilol NDC 43063-833 by Pd-rx Pharmaceuticals, Inc., 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.

43063833 Label - 43063833

43063833 Label - 43063833

This is a description for a medicine called Carvedilol. It comes in a box of 60 tablets with a dosage of 6.25mg. The Reorder number is 110463. It is manufactured by a company called LGRS Jsdly located in Oklahoma City. The medication's National Drug Code (NDC) is 43063-833-60. The medicine is used to treat several conditions, and the dosage should be determined by your doctor. The medication may cause some side effects. If you experience any, you should contact your doctor or report them to the Food and Drug Administration (FDA). The expiration date of the product is October 2022.*

Figure 1. Survival Analysis for COPERNICUS (Intent-to-Treat) - carvedilol fig1

Figure 1. Survival Analysis for COPERNICUS (Intent-to-Treat) - carvedilol fig1

Figure 2. Effects on Mortality for Subgroups in COPERNICUS - carvedilol fig2

Figure 2. Effects on Mortality for Subgroups in COPERNICUS - carvedilol fig2

This text cannot be evaluated without any accompanying information or context. It appears to be a figure caption with information on subgroups and their effects on mortality, but without any data or further explanation.*

Figure 3. Survival Analysis for CAPRICORN (Intent-to-Treat) - carvedilol fig3

Figure 3. Survival Analysis for CAPRICORN (Intent-to-Treat) - carvedilol fig3

Figure 4. Effects on Mortality for Subgroups in CAPRICORN - carvedilol fig4

Figure 4. Effects on Mortality for Subgroups in CAPRICORN - carvedilol fig4

Chemical Structure - carvedilol str

Chemical Structure - carvedilol str

* 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.