Product Images Levothyroxine Sodium

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 9 images provide visual information about the product associated with Levothyroxine Sodium NDC 42292-039 by Mylan Institutional 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.

Levothyroxine Sodium 112 mcg (0.112 mg) Tablets Unit Carton Label - image 01

Levothyroxine Sodium 112 mcg (0.112 mg) Tablets Unit Carton Label - image 01

The text contains information about a pharmaceutical tablet product with the NDC code 4220203820. The tablets are labeled as USP and the packaging appears to be a bottle. There are also caution statements about the appropriate storage of the tablets, including protection from light, heat, and moisture. The rest of the text appears to be illegible or has been misinterpreted by the .*

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image 02

Levothyroxine Sodium 88 mcg (0.088 mg) Tablets Unit Carton Label - image 03

Levothyroxine Sodium 88 mcg (0.088 mg) Tablets Unit Carton Label - image 03

This is a description of Levothyroxine Sodium Tablets, USP in 112 mcg (0.112 mg) strength with NDC 42202-039-20. The text also includes some prescribing information such as dosage and storage instructions. However, there are some typos and formatting errors in the text, making it unclear and difficult to read in some parts.*

Unit Carton - image 04

Unit Carton - image 04

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image 05

Levothyroxine sodium tablets with a strength of 137 mcg (0.137 mg) are available as per the USP. The manufacturer is Mylan with NDC number 51079-265-08. No other information on the product is available due to poor quality.*

Unit Carton - image 06

Unit Carton - image 06

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image 07

Levothyroxine Sodium Tablets, USP with a strength of 175 mcg and a National Drug Code of 42202-040-20. The remaining text is not readable.*

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image 08

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* 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.