Product Images Gabapentin
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Product Label Images
The following 7 images provide visual information about the product associated with Gabapentin NDC 43353-190 by Aphena Pharma Solutions - Tennessee, Llc, 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.
This is a graph depicting the weekly mean pain scores for Study 1. The graph shows the results of a 4-week dose titration period, followed by a 4-week fixed dose period. The placebo is represented by an "O" symbol and gabapentin, 3600 mg/day, is represented by an "&" symbol. The graph shows that gabapentin significantly reduced pain compared to the placebo.*
The graph shows the results of a study (Study 2) on the mean pain score over a 3-week dose titration period and a 4-week fixed dose period. The study used a placebo and two different doses of Gabapentin (1800 mg/day and 2400 mg/day). The graph shows a significant decrease in pain scores for both Gabapentin doses compared to placebo. The text also includes a copyright symbol and some characters that are not clear, followed by a footnote indicating that p<0.01.*
The text provides the results of controlled studies conducted to evaluate the effectiveness of PBO and GBP for treating PHN. The results are presented in Figure 3, which displays the percentage of patients who responded positively to treatment (defined as those with a reduction in pain score of >50%) at the study endpoint. The data from two separate studies are presented. There are also symbols indicating the level of statistical significance (p values) for the results.*
The text provides data for responder rates in patients receiving Gabapentin as an adjunctive therapy in partial seizures. The data is presented as a difference from placebo by dose and study. Daily doses are listed as 600mg, 900mg, 1200mg, and 1800mg.*
* 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.