Product Images Yonsa
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 5 images provide visual information about the product associated with Yonsa NDC 51407-990 by Golden State Medical Supply, 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.
51407-990-12OL Yonsa Tablets Sun Rev. 0625.jpg - 51407 990 12OL Yonsa Tablets Sun Rev. 0625
This text provides information about a medication called YONSA, which is in the form of 125 mg tablets. It includes instructions on dosage, storage conditions, and precautions such as keeping the medication out of reach of children. It also warns women who are pregnant or may become pregnant to avoid handling YONSA without gloves. Additionally, the text mentions that the tablets should be swallowed whole and not crushed, chewed, or dissolved. The medication is manufactured by Sun Pharmaceutical Industries Limited and marketed by GSMS, Incorporated. It highlights that YONSA may have different dosing and food effects compared to other abiraterone acetate products.*
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This text appears to be mentioning the names "Abiraterone Acetate" and "Placebo" in the context of a comparison involving time to death in months. It may be part of a medical or research study evaluating the effectiveness of Abiraterone Acetate compared to a placebo.*
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This data appears to show the statistics regarding subjects who did not experience death during a certain period of time in a study comparing Placebo with Abiraterone Acetate. The numbers seem to represent the time in months from randomization.*
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This text shows a comparison between Abiraterone Acetate and Placebo in a study with the results represented in a table. The results are categorized by months from randomization and include data such as the number of subjects without Radiographic Progression at each time point.*
* 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.
