Product Images Gabapentin

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Product Label Images

The following 7 images provide visual information about the product associated with Gabapentin NDC 71205-671 by Proficient Rx Lp, 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.

71205-671-72 - gabapentin 400mg 30s

71205-671-72 - gabapentin 400mg 30s

This is a prescription drug packaging for Gabapentin capsules. It is manufactured by ScieGen Pharmaceuticals, Inc. with a dosage of 400mg per capsule. The package includes 120 capsules with the identifier numbers SN# MASTER, Lo1#00060, and NDC 71205-671-72. The capsules are off-white with opaque orange-colored bodies and caps, labeled "SG" and "181" respectively. The packaging also recommends providing a medication guide to each patient upon dispensing. The lot number is #00000 and the expiration date is not clearly specified, likely obscured by errors.*

Figure 1 - image 01

Figure 1 - image 01

This appears to be a chart or table heading related to pain management. It mentions mean pain score and different doses/treatments such as placebo and gabapentin. It also seems to have a baseline and track data over several weeks. However, without further context or information it is difficult to provide a more specific or detailed description.*

Figure 2 - image 02

Figure 2 - image 02

This is a graph that shows the mean pain score over a 7-week fixed dose period of a drug. Four different doses were tested: a placebo and gabapentin doses of 1800 mg/day, 2400 mg/day, and an unknown dose. The graph indicates the pain was measured at baseline and weeks 1 through 7.*

Figure 3 - image 03

Figure 3 - image 03

Cockcroft and Gault Equation - image 04

Cockcroft and Gault Equation - image 04

Gabapentin Structural Formula - image 05

Gabapentin Structural Formula - image 05

image-09 - image 09

image-09 - image 09

* 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.