Product Images Nexium
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 12 images provide visual information about the product associated with Nexium NDC 0186-5040 by Astrazeneca Pharmaceuticals Lp, 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.
The given text "Percent of Patients Symptom-Free" suggests that the subsequent numbers represent the percentage of patients who are symptom-free. Therefore, the first number "100" might indicate that all patients are symptom-free. However, since there is no further context, it is not clear what kind of symptoms are being referred to, or what population is being studied. The remaining numbers "21" and "28" may be percentages representing the proportion of patients who are symptom-free in different groups or at different time points. Further information is required to give a more accurate interpretation.*
Nexium is a delayed-release oral suspension medication that comes in child-resistant packets containing 10 mg of esomeprazole magnesium. Each carton has 30 packets, with a total of 30 single-dose packets per carton. The enclosed medication guide should be given to each patient. This product is available only with a prescription.*
Nexium is a medication that comes in child-resistant packets and is taken as delayed-release oral suspension. Each packet of the medication has 20mg of esomeprazole magnesium. The medication guide should be given to each patient who is prescribed Nexium. The National Drug Code for the medication is 0186-4020-01.*
This text is a label of a medication containing the drug Nexium (esomeprazole magnesium), which is a delayed-release oral suspension presented in packs of 30 sachets. The label also includes a prescription code and instructions to dispense the enclosed Medication Guide to each patient.*
* 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.