Diagnosis of Errors

In experimental work, three sources of errors may be encountered those having their origin in the nature of the injected sample material, those due to malfunction of the FI A apparatus, and those due to poorly designed chemistry. 

Sample pretreatment, like dilution, neutralization, and filtration, may sometimes be necessary prior to injection of a sample into a FIA system, although highly concentrated, very acid (basic) or viscous samples may be directly diluted by injecting microliter volumes into confluence-type manifolds. Filtration cannot be avoided if suspended solids are present initially, yet precipitates may also be formed during the analytical procedure. Not only can the presence of solid particles lead to possible blocking of the tubes, but they can equally well interfere with the transducers, especially in optical systems. Although sample cleanliness is usually easy 

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Johnson, W. L. Intention-based diagnosis of errors in novice programs. Computer Science Dept, Research Report No. 395 Yale University, New Haven, Connecticut, 1985. 

To be understood for critical evaluation, losses have to be identified, named, and described. Losses cost injury, pain, mental anguish, money, and effort. When losses are understood, the means to prevent them are more rationally generated. Without enlightened diagnosis of errors, disciplined treatment of causes is not likely to foUow. 

These two examples hint at a few of the reasons for the importance of knowledge-based systems. A medical faciHty may handle hundreds of infectious disease cases a year. Speedy, accurate diagnosis of these cases, aided by a system such as Mycin, may help the medical faciHty handle more patients, more effectively. Likewise, configuring large computer systems composed of many components can be a time-consuming and error-prone task. 

For critical, high consequence systems, simulators are useful to practice diagnosis and correction of errors and abnormal conditions in emergency conditions (CCPS, 1994a). 

The confusion matrix (NSAC-60) is a method that identifies potential operator errors lemming from incorrect diagnosis of an event. It can be used to identify the potential for an operator to conclude that a small LOCA has occurred, when it is actually a steam line break. This provides a method for identifying a wrong operator response to an off-normal plant condition. It is particularly useful in Step 5 of the. SHARP procedures, Documentation requirements are presented in Table 4.5-2. 

Advocates of the global approach would argue that human activities are essentially goal-directed (the cognitive view expressed in Chapter 2), and that this cannot be captured by a simple decomposition of a task into its elements. They also state that if an intention is correct (on the basis of an appropriate diagnosis of a situation), then errors of omission in skill-based actions are imlikely, because feedback will constantly provide a comparison between the expected and actual results of the task. From this perspective, the focus would be on the reliability of the cognitive rather than the action elements of the task. 

Another example of error in the estimation of the prevalence of certain disorders by using systems of diagnosis that fail to take cultural characteristics into account is the case of ataque de nervios among Puerto Ricans. 

A modified Youden two sample quality control scheme is used to provide continuous analytical performance surveillance. The basic technique described by other workers has been extended to fully exploit the graphical identification of control plot patterns. Seven fundamental plot patterns have been identified. Simulated data were generated to illustrate the basic patterns in the systematic error that have been observed in actual laboratory situations. Once identified, patterns in the quality control plots can be used to assist in the diagnosis of a problem. Patterns of behavior in the systematic error contribution are more frequent and easy to diagnose. However, pattern complications in both error domains are observed. This paper will describe how patterns in the quality control plots assist interpretation of quality control data. 

The analysis of amino acids has been the first-line approach for the diagnosis of inborn errors of metabolism in most laboratories ever since the end of the 1950s, and it is expected to continue to play this role for a long time. Both the plasma and the CSF amino acid profile are now well known and interpretation should not pose any problems. A correct diagnosis requires adequate pattern recognition [7]. 

Organic acid analysis is one of the most challenging tests apphed to the diagnosis of inborn errors of metabolism. Several hundreds of compounds are excreted in the urine of individuals free of apparent disease, and the excretion of informative markers could be marginal depending on clinical status. Therefore, pattern recognition and descriptive interpretation are essential for proper utilization of this test. 

This chapter deals with the assays used for the diagnosis of three groups of inborn errors of metabolism of carbohydrates, i.e. ... 

Analysis of BAs in urine, serum, bile and stool is crucial for the diagnosis of inborn errors of BA metabolism. It is also helpful for understanding their pathophysiological role in acquired hepatic diseases and for monitoring the effects of therapy on metabolism. Several different inborn defects affecting BA synthetic pathway, have been described over last 20 years [7]. 

Recently, more rapid LC-MS methods have been developed to simultaneously detect free BAs and their conjugates. LC-MS has proven to be a reliable technique for the diagnosis of inborn errors of BA metabolism, even with very little sample amounts available. The specificity, sensitivity, reproducibility, speed and the ability to perform simultaneous measurements characterise routine analysis of free BAs and their conjugates in LC-MS. 

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