Product Images Hempvana Knee Bird Ultra Strength Pain Relief

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Product Label Images

The following 2 images provide visual information about the product associated with Hempvana Knee Bird Ultra Strength Pain Relief NDC 73287-020 by Telebrands Corp, 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.

4 Lidocaine 1 Menthol Patch POUCH v5 page 001

4  Lidocaine 1  Menthol Patch POUCH v5 page 001

The text provides detailed information about the Hempvana Ultra Strength Pain Relief Patches. The patches contain lidocaine and menthol as active ingredients and are designed to provide relief from pain. The text includes drug facts, warnings and directions on the usage of the patches. The patches are for external use only, and it also warns the users not to use them on wounds, broken or irritated skin, or blistered areas. Overall, the text provides a thorough description of the product, its usage instructions, and warnings associated with its use.*

New KB 4.5x1.3x6.5 inch v10 page 001

New KB  4.5x1.3x6.5 inch v10 page 001

This is a product description for a pain relief patch that contains 4% Lidocaine and 1% Menthol as active ingredients, sold under the brand name "Hempvana". It is for temporary pain relief, and is only for external use. The patch is applied to clean and dry affected areas, and can be used up to three to four times daily. The maximum application time for one patch is eight hours. This product should not be used over raw surfaces or blistered areas, nor should it be used over the same area with other topical analgesics. The inactive ingredients include Aluminum Glycinate, Carboxymethylcellulose Sodium and Cannabis Sativa (Hemp) seed oil. This product includes drug facts, warnings, directions, and information about inactive ingredients. The manufacturer is Telebrands Corp, and the product is made in China.*

* 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.