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Mr. McShane I’am an ex police officer in TX. I can say ex because I was terminated after guy that I thought was my friend claimed that he found marijuana in his residence that I left behind. After receiving a copy of the statement that he submitted during his interview by the departments internal affairs officer I found that he was lying through out the statement. I was then called in by the chief of police, who ordered me to submit to a urine drug screen. Well after about a week it came back that I was positive for marijuana metabolite and (THC), and opiates (hydrocodone). I am currently taking prescribed meds, hydrocodone, meloxicam, ibuprofen and tramadol for pain managent for a injury to my hand. I was also taking a drug called Protonix (Pantroprazole) for a serious case of acid reflux that I had to be hospitialized for. It was also prescribed by my doctor. through research I found out that the Protonix have caused false positives for THC on urine test. I told the chief and he told me to call the lab and give them the name of all my prescriptions. I spoke with a lab tech and gave him the name of al my meds. I mentioned to him about my research on he protonix and he had never heard of the drug and asked me to spell it for him. He then told me that it was positive and did not elaborate much, other than he would talk to the doctor in charge of the lab, and call me back the next day. After getting a call from him he then told me that the doctor told him the there is no way for false positive because the test was done using the GC/MS method. I have about 8 days to decide, and let my union attorney know if I want to go to arbitration, or if I even have a valid case. Please Help!!!
This is one of a set of measures used to assess the accuracy of a diagnostic test (see sensitivity, negative predictive value and positive predictive value). Specificity is the proportion of people without a disease who are correctly identified as not having that disease by the diagnostic test. For example, if a test has a specificity of 95%, this means that it correctly identified 95% of the people who did not have the disease, but that 5% of people without the disease were incorrectly diagnosed as having the disease (these people were ‘false positives’ on the test).