Product Images Divalproex Sodium

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Product Label Images

The following 5 images provide visual information about the product associated with Divalproex Sodium NDC 63629-4698 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 - divalproex fig1

Figure 1 - divalproex fig1

This is a graph showing the percentage reduction in CPS rate for patients taking Divalproex Sodium Delayed-Release Tablets compared to those taking placebo. The graph portrays the percentage of patients with improvement, no change, and worsening.*

Figure 2 - divalproex fig2

Figure 2 - divalproex fig2

The text describes a figure (Figure 2) that shows the percentage reduction in CPS rate for high and low dose divalproex sodium delayed-release tablets. The figure indicates that 50% of the patients have shown an improvement in CPS rate while some have shown no change or worsening of the condition.*

Figure 3. Mean Reduction In 4-Week Migraine Headache Rates - divalproex fig3

Figure 3. Mean Reduction In 4-Week Migraine Headache Rates - divalproex fig3

This seems to be a mix of numerical and textual data. It is unclear what this information represents, and without additional context, it is difficult to provide a useful description.*

Structure - divalproex str

Structure - divalproex str

lbl636294698

lbl636294698

This is a medication package of Divalproex Sodium 500mg ER Tablets that contains 60 tablets. The medication is manufactured by Aurobindo Pharma Limited and is packaged by Bryant Ranch Prepack Bunbank. It is recommended to store the medication at room temperature of 20°-25°C (68°-77°F) and keep it tightly closed. The medication can be used as a substitute for Depako-t, a medication with similar composition. The text includes product details such as the shape (gray oval) and the strength (500mg).*

* 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.