Product Images Flomax

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Product Label Images

The following 8 images provide visual information about the product associated with Flomax NDC 67296-1803 by Redpharm Drug, Inc., 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.

EPM 30 COUNT - 67296 1803 3

EPM 30 COUNT - 67296 1803 3

FLOMAX is a prescription medication with a strength of 0.4mg and contains 30 capsules. The lot number is UC5931X1 and the expiration date is 04/21. It is distributed by Redpharm Drug and manufactured by Sanofi-Aventis U.S. LLC. The usual adult dosage may be found in the package insert. The NDC number is 67296-1803-3 and the UPC barcode is 67296 18033 8. Storage at controlled room temperature is recommended.*

Chemical Structure - flomax 01

Chemical Structure - flomax 01

Figure 1 - flomax 02

Figure 1 - flomax 02

Figure 2A - flomax 03

Figure 2A - flomax 03

This appears to be a graph or chart with various treatments and their outcomes measured over a period of weeks. The x-axis shows the duration of treatment in weeks and the y-axis measures "Mean Change". There are three treatments represented: 0.08mg, 0.04mg, and placebo. It is not clear what is being treated or what "Mean Change" refers to.*

Figure 2B - flomax 04

Figure 2B - flomax 04

The text seems to be a statistical report about the mean change in treatment over a certain duration of weeks. The values appear to be organized into columns labeled "Treatment", "Duration of Treatment (weeks)", and "Mean Change". However, the text is not very clear, with many instances of "-0-0-0-" and "Lyl ohmg" which make it difficult to interpret.*

Figure 3A - flomax 05

Figure 3A - flomax 05

Figure 3B - flomax 06

Figure 3B - flomax 06

PRINCIPAL DISPLAY PANEL - 0.4 mg Capsule Bottle Label - flomax 07

PRINCIPAL DISPLAY PANEL - 0.4 mg Capsule Bottle Label - flomax 07

* 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.