Product Images Quip Anticavity

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Product Label Images

The following 2 images provide visual information about the product associated with Quip Anticavity NDC 69261-006 by Quip Nyc 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.

2012 quip Mint - 201218 quip Mint

2012 quip Mint - 201218 quip Mint

This is the description of an Anticavity Fluoride Toothpaste available in mint flavor. The toothpaste is capable of preventing dental cavities, and its active ingredient is Sodium Fluoride at 0.24%. The recommended usage amount is a pea-sized dollop, twice a day, and it comes in a 3-month supply. The toothpaste has various inactive ingredients like Glycerin, Water, Hydrated Silica, Sorbitol, Erythritol, Sodium Lauroyl Sarcosinate, Flavor, Xylitol, Cellulose Gum, Sucralose, Titanium Dioxide. It should be kept at a safe distance from children under six years of age, and in case of accidental swallowing, medical assistance should be prioritized. The product weighs 4.7 OZ, and once the toothpaste finishes, its tube can be recycled with #2 plastics. For more information or questions, one can contact quip NYC Inc. at [email protected].*

Quip Mint 12-2020 - Quip Mint 12 2020

Quip Mint 12-2020 - Quip Mint 12 2020

This is the description for Quip Toothpaste which focuses on the product's ingredients and features, including sodium fluoride and xylitol to prevent cavities and reduce oral bacteria, vegan and SLS-free (contains no sodium lauryl sulfate), and packaged in a 100% recyclable paper packaging. The toothpaste is a 3-month supply and has a net weight of 4.7 oz (133g). It includes directions and warnings for use, and its inactive ingredients include glycerin, water, hydrated silica, sorbitol, and cellulose gum. The toothpaste is distributed by quip NYC Inc and is available for purchase on the website getquip.com.*

* 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.