Product Images Medi-first Plus Burn With Lidocaine

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Product Label Images

The following 2 images provide visual information about the product associated with Medi-first Plus Burn With Lidocaine NDC 47682-985 by Unifirst First Aid Corporation, 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.

MF Burn - MF Burn26073 Nantong UltraD 1 12 2023 Label

MF Burn - MF Burn26073 Nantong UltraD 1 12 2023 Label

This is a drug fact sheet for Medifirst Burn Cream with Lidocaine. It contains warnings, usage instructions, and information on active and inactive ingredients. It is designed for external use only and is only to be used on minor burns. The formula includes antiseptics and painkillers, and it listed by item number 26073. The text also includes a phone number for the product's manufacturer, but is not available in its entirety due to errors.*

MFP Burn - MFP Burn93973 Nantong UltraD 1 12 2023 (002) Label

MFP Burn - MFP Burn93973 Nantong UltraD 1 12 2023 (002) Label

This appears to be the packaging and drug facts for a burn cream with Lidocaine, which is an over-the-counter product used for temporary relief of pain associated with minor burns and protection against harmful bacteria. The package contains 25 packets of 0.9g (1/32 oz) each, and the active ingredients are listed as Benzalkonium Chloride 0.13% (topical antiseptic) and Lidocaine HCl 0.5% (topical analgesic). The cream should be applied externally up to 4 times daily, but not to open wounds, eyes, or large areas of skin. If ingested, medical help or contact with a Poison Control Center should be sought immediately. If symptoms worsen, persist for more than 7 days, or reoccur within a few days of use, the user should stop and contact a doctor. Other details about the cream's packaging, non-medicinal ingredients, and manufacturer contact information are also provided.*

* 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.