Absent Clinical Data

A clinician may be of the belief that a piece of clinical data should exist in the Systran when, for whatever reason it does not or it cannot be accessed. Alternatively a clinician may assume that if some relevant data does exist that this will be brought to his/her attention in the course of interacting with the system. Both of these scenarios result in situations where information is unavailable to the clinician requiring them to make assumptions or source the data from elsewhere. 

There are a number of underlying causes which could result in information being unavailable  

---

Clinical data can be absent because all or part of the system is unavailable or slow, access to it is denied, its presence it not noticed or it fails to be delivered. Clinical data can be misleading because of how it is presented, contextualised, identified or entered. Additionally information can become corrupted, fail to be communicated or the system provide inaccurate advice such that decision making is compromised. 

Preclinical data are suggestive th the technology might be useful for breast cancer detection but clinical data are absent or very sparse for the given application. 

Other data arise as scores. These are frequently as a result of the need to provide a measure of some clinical condition such as depression or anxiety. The Hamilton Depression (HAM-D) scale and the Hamilton Anxiety (HAM-A) scale provide measures in these cases. These scales contain distinct items which are scored individually and then the total score is obtained as the sum of the individual scores. For the Hamilton Depression scale there are usually 17 items depressed mood, self-depreciation and guilt feelings, etc., each scored on a three-point scale or on a five-point scale. The five-point scales are typically scores 0 = absent, 1 = doubtful to mild, 2 = mild to moderate, 3 = moderate to severe and 4 = very severe while the three-point scales are typically 0 = absent, 1 = probable or mild and 3 = definite. 

Early clinical studies are performed in cancer patients in hospitals instead of healthy volunteers, and in specialized Phase I units. Selection criteria for patients entered into cetuximab Phase I studies included various important factors such as disease state, life expectancy (>3 months), prior treatment, organ function, age, tumor type and target (i.e., EGFR expression). Therefore, pharmacokinetic (PK) data obtained from these individuals is confounded by numerous factors, a fact usually absent in conventional studies with tightly controlled, well-selected healthy subjects performed for non-oncologic drags. 

Is idiopathic hypercalcemia of infancy a new disease or is it one whose anonymity has for long been cloaked under terms such as marasmus To this question no categorical answer can be given. In the earlier literature cases were reported which, from a clinical point of view, might well have been idiopathic hypercalcemia of infancy. Usually, however, the biochemical data essential for conclusive diagnosis have been absent. 

Consistent with the monkey data, PS ODN and 2 -MOE ASO have been largely absent of hepatotoxicity in clinical trials [47,48,64,65].The only notable exception was the report of mild and transient increases in ALT of patients treated with anti-HIV PS ODN, GEM 91 [66]. This was also likely attributable to the proinflammatory effects that were present for this early generation of ASO comprised of 27 nucleotide residues along with CpG motiffs. 

It is important to evaluate the clinical presentation of the patient as well as laboratory data to determine and guide the administration of antivenom. However, antivenom is not required in all patients who are envenomated and may not be necessary if there is no significant tissue swelling, systemic symptoms are absent, and laboratory parameters are normal. 

There may, in addition, be conditional branches in the process which only occur under certain circumstances, the medication is contraindicated, the patient is absent when the medication needs to be administered, etc. The processes can usually be mapped out quite easily using nse case diagrams, flow charts, user stories or other tools. What is key when looking at the clinical bnsiness process is that the steps are agnostic abont the tools which are nsed to achieve the objective, i.e. whether a particular electronic solntion is ntilised, paper systems or by some other means. The processes remain entirely in the clinical domain without any mention of screens, interfaces, data or hardware. 

A few years ago Maas and Schubothe (1973) and Maas et al. (1974,1975) described a syndrome clinically resembling LE in which antimitochondrial antibodies were present, but not antinuclear antibodies. Skin manifestations were, however, absent. Not long afterwards it was pointed out that in many of these cases of pseu-do-LE syndrome there was probably a link with the substance Venopyronum (intended for diseases of veins and containing l,4-diphenyl-3,5-dioxo-pyrazolidine together with certain plant extracts). The reported data show that the patients involved had been taking this product for several months or years (Maas and Schubothe 1973 Maas et al. 1974, 1975 Muller-Schoop et al. 1975 Schwarz and JosT 1975). 

The wear performance of the sliding couple ceramic femoral ball/acetabular cup liner is crucially important, as the synovial fluid lubricant that is present in the natural hip joint is absent from the artiflcial system. Hence, the coefficient of friction must be as low as possible. Data relating to the linear wear of clinically established wear couples, obtained during wear screening tests, are hsted in Table 10.8 (Heimann and WiUmann, 1998). 

Using speed as a crash cause example was not done by chance. It was picked because speed is one behavior or human factor that can be (relatively easily) observed both in crash data and in traffic flow data. Environmental and vehicle factors can also be obtained in both types of data and are prime candidates for evaluation via the statistical approach. Other measures are more difficult. For example, alcohol involvement in crashes can be assessed if alcohol levels are obtained from the crash involved drivers (not too difficult to do) and from the general traffic population (much more difficult since it requires random stopping of drivers to check for their alcohol levels - even if they did not commit any traffic violation). Still other behaviors are practically impossible to evaluate statistically. Unfortunately the behaviors that fall into this category are the most frequent ones to emerge in the clinical evaluations attentional, perceptual, and decision failures. They are difficult to ascertain in the crash data (and are typically absent in police crash reports), and absolutely impossible to determine from the traffic population data. 

---