Product Images Duloxetine
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 11 images provide visual information about the product associated with Duloxetine NDC 50090-4709 by A-s Medication Solutions, 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.
The graph displays the proportion of patients with relapse over time from randomization to relapse in days. The y-axis represents the proportion of patients, while the x-axis represents the days. The proportion of patients with relapse starts from 0% at 0 days, increases steadily, and reaches 10% at 300 days.*
This is a graph that shows the percentage of patients improved with different dosages of Duloxetine and Placebo. The x-axis represents the percent improvement in pain from baseline and the y-axis represents the number of patients. There are four lines on the graph, representing different treatments: Duloxetine 60mg BID, Duloxetine 60mg 0D, Duloxetine 20mg 0D, and Placebo. The graph shows that Duloxetine 60mg BID had the highest percentage of patients who improved from the baseline.*
This appears to be a chart of some sort showing the percentage of patients who have reported improvement in pain along with their corresponding baseline percentage. Without more context or information, it is difficult to determine what the numbers represent or what the purpose of the chart is.*
The text suggests a chart or graph showing the "Percentage of Patients improved" with different treatments, including a placebo and a treatment called Dulcseiass120. The chart measures improvement by percentage, with the left axis ranging from 0 to 100, and the bottom axis showing different measures of pain improvement from baseline. There is no further context to determine what specific condition or ailment this chart is addressing.*
* 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.