Interpretation bias

Cores from the same case Ideally, if same-patient cores are to be placed on the same block, they should be dispersed on the block. This will decrease the risk of interpretation bias. However, some workers prefer this arrangement so that they can immediately... 

Urinary tract Polyoma (BK) virus-associated nephropathy (BKVAN) in kidney transplant patients can be treated with cidofovir. In a retrospective non-randomized observational study of functional and pathological variations in 12 patients with BKVAN (1.4% of all renal transplant patients included in the observational analysis) and 24 with BK vire-mia alone (5.2% of the cohort), the indication for cidofovir was not evidence-based and treatment was determined on an individual case basis 2% The authors reported that cidofovir-treated patients did not have a reduction in BK viremia and also had deterioration in renal function (mean serum creatinine change 150 pmol/1) compared with those who did not receive cidofovir (mean creatinine change - 400 pmol/1). In view of the small sample size, the retrospective nature of this study, and the lack of (1) randomization, (2) a clearly defined treatment protocol for the condition in those patients studied, and (3) a clearly defined dosing regimen, these findings must be considered with extreme caution. The study was susceptible to both selection and interpretation bias. 

Internal methods of quality assessment should always be viewed with some level of skepticism because of the potential for bias in their execution and interpretation. For this reason, external methods of quality assessment also play an important role in quality assurance programs. One external method of quality assessment is the certification of a laboratory by a sponsoring agency. Certification is based on the successful analysis of a set of proficiency standards prepared by the sponsoring agency. For example, laboratories involved in environmental analyses may be required to analyze standard samples prepared by the Environmental Protection... 

Analysts The above is a formidable barrier. Analysts must use limited and uncertain measurements to operate and control the plant and understand the internal process. Multiple interpretations can result from analyzing hmited, sparse, suboptimal data. Both intuitive and complex algorithmic analysis methods add bias. Expert and artificial iutefligence systems may ultimately be developed to recognize and handle all of these hmitations during the model development. However, the current state-of-the-art requires the intervention of skilled analysts to draw accurate conclusions about plant operation. 

Assuming = 0 will potentially add bias to the interpretation of plant measurements. Further, the plant bias may to some extent mask the error in the measurements. While the designer may have envisioned a constant set of conditions or a specified time dependence, it is likely that the actual operation changes due to external factors. 

This is a formidable analysis problem. The number and impact of uncertainties makes normal pant-performance analysis difficult. Despite their limitations, however, the measurements must be used to understand the internal process. The measurements have hmited quahty, and they are sparse, suboptimal, and biased. The statistical distributions are unknown. Treatment methods may add bias to the conclusions. The result is the potential for many interpretations to describe the measurements equaUv well. 

The following presents guidelines for identifying, validating, reconciling, rectifying, and interpreting plant measurements to remove some of the bias from the conclusions. 

An example adapted from Verneuil, et al. (Verneuil, V.S., P. Yan, and F. Madron, Banish Bad Plant Data, Chemical Engineeiing Progress, October 1992, 45-51) shows the impact of flow measurement error on misinterpretation of the unit operation. The success in interpreting and ultimately improving unit performance depends upon the uncertainty in the measurements. In Fig. 30-14, the materi balance constraint would indicate that S3 = —7, which is unrealistic. However, accounting for the uncertainties in both Si and S9 shows that the value for S3 is —7 28. Without considering uncertainties in the measurements, analysts might conclude that the flows or model contain bias (systematic) error. 

However, other bias errors are so substantial that their presence will significantly distort any conclusions drawn from the adjusted measurements. Rectification is the detection of the presence of significant bias in a set of measurements, the isolation of the specific measurements containing bias, and the removal of those measurements from subsequent reconcihation and interpretation. Significant bias in measurements is defined as gross error in the literature. 

Various theoretical interpretations of the bias of norboranone 25 have been proposed. Two basic explanations have been suggested, i.e., torsion-based arguments [91] and stereoelectronic arguments [1, 92-95]. 

In case (1) the different samples must be individually prepared. In the strictest interpretation of this rule, every factor that could conceivably contribute to the result needs to be checked for bias, i.e., solvents, reagents, calibrations, and instruments. That this is impractical is immediately apparent, especially because many potential influences are eliminated by careful exper-... 

In recent years some theoretical results have seemed to defeat the basic principle of induction that no mathematical proofs on the validity of the model can be derived. More specifically, the universal approximation property has been proved for different sets of basis functions (Homik et al, 1989, for sigmoids Hartman et al, 1990, for Gaussians) in order to justify the bias of NN developers to these types of basis functions. This property basically establishes that, for every function, there exists a NN model that exhibits arbitrarily small generalization error. This property, however, should not be erroneously interpreted as a guarantee for small generalization error. Even though there might exist a NN that could... 

Randomization refers to the process of assigning subjects by chance to treatments. This eliminates known and unknown sources of bias that could interfere with accurate interpretation of the study results. The main problem that randomization is intended to prevent is bias in subject selection. Without randomization, investigators might consciously or subconsciously select subjects to receive the active treatment, which, they believe, are most likely to respond. History shows that uncontrolled studies are much more likely to provide exaggerated support in favor of the effectiveness of a treatment than properly controlled trials (Pocock, 1983). Therefore, whenever possible, randomization should be used in order to help insure a fair and unbiased evaluation of the intervention under study. 

Historical data on the indicator. Existing information on the statistical variation, bias, and other interpretational attributes of potential biological indicators should be examined and considered in the design of a sampling program for assessing trends in mercury bioaccumulation. 

The MEM is a powerful new method which is especially useful in cases with limited data sets (powder diffraction). Monte Carlo simulations have shown that the MEM introduces systematic features into the reconstructed density and caution should be exercised when interpreting fine details of an MEM density. It must be emphasized that because the present MEM algorithms do not contain any models, they cannot filter out inconsistencies in the data stemming from systematic errors. The MEM densities may therefore contain non-physical features not only because of systematic bias in the calculation but also because of systematic errors in the data. 

In the case of a large bias, the second term of Eq. (9.19) can exceed the first one and so the information content formally would become negative. Although this could be interpreted as misinformation, negative information contents are unusual in information theory itself. For this reason, Eckschlager and Stepanek [1985] introduced... 

As you are no doubt aware, integrals are one of the key parameters in the interpretation of proton spectra and are pivotal in quantification. They measure the area under a peak and this is directly proportional to the number of protons (in the case of proton NMR) in that environment. Most software will automatically try to identify the peaks in your spectrum and integrate them for you. If you need to do it yourself, then it is a fairly trivial matter of defining the start and end point of the integrals of interest. The only complication is that you may need to tweak the slope and bias of the integral. This should be unnecessary if you have got the phase and baseline of your spectrum correct. If you find that you need to adjust slope and bias, we suggest that you go back and try to sort out baseline and phase a bit better. 

Since the scatter of experimental points for total conversion is both above and below the curve for the demineralized sample, it is not possible to assign the behaviour of the untreated coal to either catalyst blinding or enhanced catalytic effects. With regard to the yield of bitumen (Fig. 8), the bias on the high side in yield could be interpreted to suggest that some catalytic... 

The main potential drawback of genetic association studies is the bias resulting of the lack of ethnic matching between the groups under study, which can lead to spurious results [42, 43]. This bias is the best known and acute problem of this design and affects the interpretation of the results. When comparing two groups... 

In order to ameliorate the sharply sloping background obtained in an STS spectrum, the data are often presented as di,/dFh vs. Vb, i.e. the data are either numerically differentiated after collection or Vb has a small modulation applied on top of the ramp, and the differential di,/d Vb is measured directly as a function of Vb. The ripples due to the presence of LDOS are now manifest as clear peaks in the differential plot. dt,/dFb vs. Vb curves are often referred to as conductance plots and directly reflect the spatial distribution of the surface electronic states they may be used to identify the energy of a state and its associated width. If V is the bias potential at which the onset of a ripple in the ijV plot occurs, or the onset of the corresponding peak in the dt/dF plot, then the energy of the localised surface state is e0 x F. Some caution must be exercised in interpreting the differential plots, however, since... 

A sample is selected by a random process to eliminate problems of bias in selection and/or to provide a basis for statistical interpretation of measurement data. There are three sampling processes which give rise to different types of random sample ... 

BIA is a simple, noninvasive, and relatively inexpensive way to measure LBM. It is based on differences between fat tissue and lean tissue s resistance to conductivity. Fluid status should be considered in interpretation of BIA results. 

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