Interpretive false positives

Drug/Lab test interactions Diagnostic pregnancy tests based on hCG may result in false-negative or false-positive interpretations in patients on promethazine. 

Interpretation While good batches of the quality produced (= 99.81% purity) have a probability of being rejected (false negative) less than 5% of the time, even if no replicates are performed, false positives are a problem an effective purity of /.t = 98.5% will be taxed acceptable in 12.7% of all cases because the found Xmean is 99% or more. Incidentally, plotting 100 (1 - p) versus /x creates the so-called power-curve, see file POWER.xls and program HYPOTHESlS.exe. 

Babson proposed a-naphthyl phosphate as an essentially specific substrate for the activity of prostatic acid phosphatase in serum (104). However Marshall, Price, and Amador found that this substrate is not specific for the prostatic enzyme because urine of human females contain 50 times more acid a-naphthyl phosphatase than male serum and 50% as much activity as male urine. Platelets have significant activity and the serum activity can increase to abnormal values following clotting. These workers also observed elevated activities in females with skeletal metastases of the breast. In 50 hospitalized male patients who had no evidence of prostatic cancer and 25 hospitalized female patients, the incidence of false positive results was 12%, a magnitude sufficient to preclude meaningful clinical interpretation (105). 

A systematic study was carried out using in parallel 50 standard solutions for each concentration of three natural colorants (curcumin, carminic acid, and caramel as yellow, red, and brown, respectively). No false positive results for synthetics were obtained up to concentrations of 15 and 20 ng/ml for natural red and yellow colorants, respectively, or 110 ng/ml for natural brown colorant. The concentrations have to be high enough to prove that the screening method is able to accurately discriminate natural and synthetic colorants. To make a clear interpretation of the quantitative UV-Vis spectrum, linear regression analysis was used. Quantitative UV-Vis analysis of a dye ° can be calculated according to the following formula ... 

Clinical and analytical interpretation of false-negative and false-positive results. 

Due to false positives, zinc may confound interpretation of the paralytic shellfish poisoning (PSP) mouse bioassay, one of the routine tests used to measure shellfish safety for human consumption. For example, mice injected intraperitoneally with extracts of healthy oyster tissues showed extreme weakness, a drop in body temperature, cyanosis, and some deaths (McCulloch et al. 1989). The threshold for a toxic PSP response corresponds to a drained tissue zinc level >900 mg/kg FW, and this overlaps the zinc concentration range of 230 to 1650 mg/kg FW (1900 to 9400 mg/kg DW) recorded in healthy oyster soft tissues (McCulloch et al. 1989). 

We found that it is necessary to run several sets of differential display primers prior to an analysis of the distribution of differential display bands. This allows for a comparison between different independent reactions using different PCR primers to assess the quality of individual cDNA samples and discriminate between sample-to-sample variability and potential positive bands that are consistently found in different repUcates. The presence or absence of a specific band in lanes corresponding to independent experimental samples indicates a reproducible difference in the relative amount of cDNA in a given sample, which should reflect differences in mRNA levels. However, the interpretation of the differential display results is not always straightforward. For example, a thick band can reflect quantitative differences in the initial concentration of a specific cDNA between samples or can represent comigration of two bands. Replication of the PCR reactions for samples that have differences in banding pattern will eliminate a significant number of false positive differential display differences. Also, in some cases, it may be informative to alter the electrophoresis conditions to maximize resolution of a band of interest prior to isolation, reamplification, and further analysis of potential positive bands. 

As in any classification problem, there is a tradeoff between the rate of recall, or proportion of correct substructures detected, and the reliability, or avoidance of false positive assertions. It is rather the exception than the rule for an observation to have a single, unequivocal explanation. When reasonable alternative interpretations are possible, a decision must be made about what to report. At one extreme, all possibilities could be asserted, ensuring 100% recall (i.e. no substructure which is actually present will fail to be detected) at the cost of a high rate of false positives. 

The effects of competition among the IR rules were explored by using the complete system, with the STIRS module disabled, to interpret the spectra of 1807 compounds from the library. For the test, we selected 500 of the 900 chemical substructures which both are chemically interesting and display at least one distinctive infrared band. Some of the selected substructures were subsets of others for example, alcohol, phenol, and primary alcohol were all in the test set. As expected, some functional groups displaying very distinctive infrared bands were detected much more reliably than others. Figure 6 shows the reliability, false positive and recall rates for a few selected functional groups. 

A rule-based infrared spectra interpreter has been developed as a major module of the program. This module has been tested as a stand-alone system, and in conjunction with STIRS. The low rate of false positive assertions is encouraging, and work continues to reduce this rate still further by incremental refinement of the knowledge base. 

Alternatively, samples are quickly screened by thermal methods, such as DSC or ITC. This alternative approach eliminates the necessity for stability set-downs hence cycle times and sample consumption are reduced. However, the data obtained are difficult to interpret and may be misleading false positives and negatives are routinely encountered [14]. 

Correct interpretation of the analytical results in the field of residue analysis is a matter of major importance. Truly positive, false-violative, false-positive, falsenegative, and truly negative are all types of test results that may be produced during food monitoring for drug residues. Truly positive is a positive test result... 

Data interpretation error—incorrect analytical data interpretation producing false positive or false negative results... 

Much more difficult to detect are data interpretation and judgment errors, such as the unrecognized false positive and false negative results and the incorrect interpretation of mass spectra and chromatographic patterns. The detection and correction of these errors is made possible through internal review by experienced analysts. 

The EPA provides further guidance for decision-making in this area when the RPD between two results exceeds 40 percent, the EPA conservatively recommends selecting the higher concentration as a true one (EPA, 1996a). This practice, however, often leads to false positive results and may be the cause of unnecessary site remediation. From a practical perspective, in the absence of matrix interferences for an analyte to be present in the sample the agreement between the two results should be better than 40 percent. If matrix interferences are obvious, a chemist experienced in data interpretation should evaluate the chromatograms and make a decision on the presence or absence of the analyte in the sample. 

Contaminated samples and method blanks and instrument memory effects (carryover) are a major source of false positive results. To determine whether data interpretation errors may have produced false positive results, the chemist examines the instrument and method blank data and answers the following questions ... 

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