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 71335-2026 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.

Figure 1. Survival Analysis for COPERNICUS (intent-to-treat) - carvedilol tab usp figure1

Figure 1. Survival Analysis for COPERNICUS (intent-to-treat) - carvedilol tab usp figure1

Figure 2. Effects on Mortality for Subgroups in COPERNICUS - carvedilol tab usp figure2

Figure 2. Effects on Mortality for Subgroups in COPERNICUS - carvedilol tab usp figure2

This appears to be a table with various categories related to gender, age, medical conditions, and medication. It also includes a hazard ratio. The text is not readable and may have been corrupted during the process.*

Figure 3. Survival Analysis for CAPRICORN (intent-to-treat) - carvedilol tab usp figure3

Figure 3. Survival Analysis for CAPRICORN (intent-to-treat) - carvedilol tab usp figure3

Figure 4. Effects on Mortality for Subgroups in CAPRICORN - carvedilol tab usp figure4

Figure 4. Effects on Mortality for Subgroups in CAPRICORN - carvedilol tab usp figure4

carvedilol chemical structure - carvedilol tab usp structure

carvedilol chemical structure - carvedilol tab usp structure

Label - lbl713352026

Label - lbl713352026

This appears to be a prescription bottle label. The medication is Coreg, with a strength of 12.5mg per tablet. The label provides instructions to store the medication at room temperature between 68-77°F and to keep all drugs out of reach of children. There are 30 tablets in the bottle, and it has a specific expiration date (MM/YY). The manufacturer listed is Rubicon Research Private Limited, and there is an NDC code provided for the medication.*

* 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.