Product Images Cyclafem 1/35
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 4 images provide visual information about the product associated with Cyclafem 1/35 NDC 76413-105 by Central Texas Community Health Centers, 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.
PRINCIPAL DISPLAY PANEL - Blister Pack Carton - cyclafem 04
The text describes a medication called Cyclafem which is an alternative for Ortho Novum 1/35. The medication contains Norethindrone 1mg and Eth Estradiol 0.035mg. Patients should take one tablet by mouth daily as directed. The medication is provided by CommUnity Care Federally Qualified Centers. A cautionary note mentions that federal law restricts the transfer of this medication to anyone other than the patient it was originally prescribed for. There is also a batch code, lot number, and expiration date mentioned.*
The is the chemical image of norethindrone ethinyl estradiol. - cyclafem135 1
This is the Figure 1 chart for the Mortality Rate. - cyclafem135 2
This is a figure depicting circulatory disease mortality rates per 100,000 woman-years categorized based on age, smoking status, and oral contraceptive use. The figure includes data on ever-users and controls, smokers and non-smokers. The information provided is adapted from a reference.*
This is an image of the blister card. - cyclafem135 3
This is a set of instructions for taking birth control pills. The instructions are divided into two categories - Sunday starters and Day 1 starters. If your period starts on a day other than Sunday, a day label strip starting with the first day of your period should be placed here. The text contains a table with days of the week and spaces to mark when pills should be taken. The instruction also says to take pills from left to right each week.*
* 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.