Product Images Colgate Anticavity Kit

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Product Label Images

The following 5 images provide visual information about the product associated with Colgate Anticavity Kit NDC 56153-002 by Ming Fai Industrial (shenzhen) Co. Ltd, 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.

10g Kit with Floss - 10g kit with floss

10g Kit with Floss - 10g kit with floss

Drug Facts - Colgate Drug Facts2

Drug Facts - Colgate Drug Facts2

This is a description for a dental product with sodium monofluorophosphate as the active ingredient. It gives directions for adults and children over 2 years old to brush teeth twice a day or as directed by a dentist or physician. Children under 6 should use a pea-sized amount and be supervised to minimize swallowing. The product is for preventing cavities and should be kept out of reach of children under 6. The drug facts information should be read before use, and if accidentally swallowed, medical help or Poison Control Center should be contacted. Questions or comments can be directed to a toll-free number provided.*

5g Label - Label2

5g Label - Label2

This text is a description of Colgate Cavity Protection Toothpaste, manufactured by Colgate-Palmolive in Thailand. It contains information on the net weight, drug facts, and how the product helps protect against cavities. The full drug facts are available on the outer wrap of the product.*

10g Label - Label3

10g Label - Label3

Product Description: Colgate Gy Anticavity Toothpaste by Colgate-Palmolive (Thailand) Ltd. It is a 10g (0.35oz) toothpaste that contains Sodium monofluorophosphate 0.76% (0.15% w/v fluoride ion) as an active ingredient. This toothpaste helps protect against cavities. Please refer to the outer wrap for full Drug Facts before use.*

5g Kit with Floss - kit with floss

5g Kit with Floss - kit with floss

* 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.