Product Images Gabapentin

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 7 images provide visual information about the product associated with Gabapentin NDC 63629-8492 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 - image 01

Figure 1 - image 01

This text appears to be a table or chart containing information related to a mean pain score. It also includes information about different types of dosage titration and treatments, such as Parkod and Gabapentin. The table includes a baseline and multiple weeks of data.*

Figure 2 - image 02

Figure 2 - image 02

The text describes a graph displaying the results of a study evaluating different doses of gabapentin and a placebo in the treatment of pain. The study includes a fixed dose period of 4 weeks and the graph shows the mean pain score over this period. The x-axis indicates the weeks, while the y-axis represents the pain score. The study includes three dose variations of gabapentin and a placebo group. The doses of gabapentin used were 0 mg/day, 1800 mg/day, and 2400 mg/day. The graph shows that the higher doses of gabapentin resulted in a lower mean pain score compared to the placebo group.*

Figure 3 - image 03

Figure 3 - image 03

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Gabapentin Structural Formula - image 05

Gabapentin Structural Formula - image 05

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Label - lbl636298492

Label - lbl636298492

Gabapentin Tablets USP are film-coated tablets that contain 600 mg of gabapentin, USP. This medication should be stored between 20-25°C, with permitted excursions to 15-30°C. It must be kept out of the reach of children and dispensed in a tight container as defined in the USP with a child-resistant closure. The product is manufactured by BRP Pharmaceuticals and comes in a packet of 100 tablets.*

* 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.