Product Images Maxalt-mlt
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 8 images provide visual information about the product associated with Maxalt-mlt NDC 78206-143 by Organon Llc, 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 text provides a graph showing the estimated probability of response to different medications, including MAXALT 5mg, MAXALT 10mg, and Placebo, at different hours post-dose. The graph's x-axis represents time in hours post-dose, while the y-axis represents the probability of response percentage. It presents useful information for evaluating the effectiveness of these medications.*
The text appears to be a graph displaying the estimated probability of remedication or rescue for three different medications (Placebo, MAXALT 5 mg and MAXALT 10 mg) over a period of time (10-24 hours post-dose). The graph also includes two lines labeled "PR" and "PP" with percentages ranging from 0 to 100. Without additional context, it is unclear what the lines represent.*
This is a chart that shows the estimated probability of response to a medication called MAXALT-MLT in 5mg and 10mg doses compared to a placebo. The chart has time on the x-axis and the probability of response on the y-axis. The probability of response ranges from 0% to 100% and the time post-dose ranges from 0 to 2 hours.*
This is a graph showing the effectiveness of a placebo and two dosages of MAXALT-MLT (5mg and 10mg) over a course of 24 hours following dosing. The efficacy is measured on a scale from 0 to 100%.*
This is a package of Maxalt 10 mg tablets from Organon. The tablets come in a pack of 18 and are equivalent to 10 mg of zolmitriptan. The package has a pharmacy label area, and the product is identifiable by its serial number, barcode, expiry and lot number.*
* 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.