Product Images Serostim
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 5 images provide visual information about the product associated with Serostim NDC 44087-0004 by Emd Serono, 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.
Figure 1 - serostim 01
This appears to be a table with headings and data related to a clinical study, comparing the effects of the drug Serostim® and a placebo on lean body mass (LBM), weight, and fat composition in patients over a period of up to 24 weeks. The table includes study identifiers and the number of patients on each treatment within each study.*
Figure 2 - serostim 02
PRINCIPAL DISPLAY PANEL - 4 mg Kit Carton - serostim 03
Serostim' 4mg is a medication intended for subcutaneous injection containing somatropin. It comes in a package that includes seven vials of Serostim and seven vials of Bacteriostatic Water for Injection, USP with 0.9% of benzyl alcohol. It's only available by prescription.*
PRINCIPAL DISPLAY PANEL - 5 mg Kit Carton - serostim 04
Serostim® is an injectable medication containing somatropin with a strength of 5mg. It is intended for subcutaneous injection and is available only through a prescription. Each package includes 7 vials of Serostim and 7 vials of sterile diluent with the National Drug Code (NDC) of 44087 00057. The manufacturer of this medication is EMD Serono.*
PRINCIPAL DISPLAY PANEL - 6 mg Kit Carton - serostim 05
This is a description of Serostim, which is a drug prescribed for subcutaneous injection. It contains 6mg of somatropin and is only available with a prescription. The package includes 7 vials of Serostim and 7 vials of sterile diluent. The NDC number for the product is 44087 00067. The text also includes the letters "EMD" and "S@RONO," but it's not clear what they refer to in this context.*
* 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.