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  5. Product Images: Sumatriptan Succinate

Product Images Sumatriptan Succinate

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 4 images provide visual information about the product associated with Sumatriptan Succinate NDC 68151-1658 by Carilion Materials Management, 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.

Label Image - 68151 1658

Label Image - 68151 1658

chemical-structure - chemical structure

chemical-structure - chemical structure

sumatriptan-figure-1 - sumatriptan figure 1

sumatriptan-figure-1 - sumatriptan figure 1

The figure presents the estimated probability of achieving initial headache response within 4 hours of treatment with oral sumatriptan. The response is based on no or mild pain. The plot is a Kaplan-Meier plot with different doses, including 25mg, 50mg, and 100mg, along with a placebo. The data used to generate this plot is obtained from pooled data of 3 clinical controlled trials, providing evidence of efficacy. The x-axis represents time in minutes from the initial dose, and the y-axis represents the estimated probability of treatment response.*

sumatriptan-figure-2 - sumatriptan figure 2

sumatriptan-figure-2 - sumatriptan figure 2

This is a graph showing the estimated probability of patients taking a second dose of sumatriptan succinate tablets or other medication to treat migraines over the 24 hours following the initial dose of the study treatment. The data is obtained from pooled trials 1, 2, and 3. The Kaplan-Meier plot is based on data obtained in three clinical trials providing evidence of efficacy with patients not using additional treatments censored to 24 hours. Patients who had no response to the initial dose are also included. No remedication was allowed within 2 hours postdose.*

* 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.