Calculation Information and Courses from MediaLab, Inc.
These are the MediaLab courses that cover Calculation and links to relevant pages within the course.
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| Calculation of CSF Cell Count In general, use the following equation to calculate CSF cell count:
(total cells counted x dilution) / (number of squares counted x volume of 1 square) = cells per microliter.For an undiluted specimen in which 10 squares are counted:
(total cells counted x 1) / (10 squares counted x 0.1 mm3 per square) = cells per microliter.Therefore, in this example:
(total cells counted) / (1 mm3) = cells per microliter.
1 mm3 is equal to 1 microliter.
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| WBC Correction for Traumatic Tap A calculation is used to correct CSF WBC counts which are falsely increased due to a traumatic tap:
WBCs added = WBC(blood) x RBC(CSF) / RBC(blood)The blood WBC count is multiplied by the ratio of the cerebrospinal fluid RBC count to blood RBC count.The result is the number of artificially introduced WBCs. The true CSF white cell count is then calculated by subtracting the artificially introduced WBCs from the actual CSF WBC count.
If the patient's peripheral WBC and RBC counts are within normal limits, some laboratories use the following formula:
Subtract one white cell from the CSF WBC count for each 750 RBC counted in the spinal fluid.
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| When evaluating the throughput of a particular method you should consider all of the following except: | View Page |
| Introduction to Statistics Statistics is the branch of mathematics that deals with the organization, analysis, and interpretation of information. It is often said that statistics is the opposite of probability. A probability question would be, "If I have a fair coin and toss it ten times, how often will I get seven or more heads?" A statistics question would be "I toss a coin ten times and get seven heads. Given this information, how likely is it that this coin is fair?"Many people are uncomfortable with statistics. Statistics is one of the more confusing mathematical subjects, and deals with many subtle concepts. Often the interpretation of a result is more difficult than calculating the result in the first place. This course will attempt to make clear both the calculations and the interpretation of those results. | View Page |
| Mean The mean, also called the arithmetic mean or the average, is simply the sum of all the data points divided by the number of points. It is denoted with . The formula for the mean is: For example, here are the number of hours that some students worked on a project: # hours worked 4 6 2 8 5 The average time spent working was: The average is the most common way of calculating central tendency. Some of its characteristics are: easy to calculate only one exists for any data set affected by all observations, and therefore strongly affected by outliers used in many statistics calculations | View Page |
| Mode The mode is the value that occurs most frequently in a data set. There can be more than one mode, if there are two or more values that are tied for being most likely. The properties of the mode are: requires no calculation not necessarily unique very insensitive to extreme values may not really be close to the center of the distribution | View Page |
| Standard Deviation Example Now we will do an example calculation of the standard deviation of a set of data. Here are the data we will use:Table VII Urea Nitrogen Concentration in 5 Employees Concentration (mg/dL) 9 7 11 13 10 | View Page |
| Using the Coefficient of Variation In the clinical laboratory, the coefficient of variation is used for two main purposes: to aid in the selection of a new method for routine use to monitor the inherent variability (precision) of a method already in routine useSelection of a new method for routine use requires comparative precision data. If the two methods being compared have different means and/or units, calculation of their CV's provides the comparison. (However, if two methods have the same units, and measure the same quantity, but have different means, this could be a sign that one of the instruments is miscalibrated.) | View Page |
| Example Regression Line Calculation The following example will clarify how to use the preceding equations: Point x y y2 (x-) (y-) (x-)(y-) (x-)2 (y-)2 xy 1 6 11 121 -8 -6 48 64 36 66 2 8 8 64 -6 -9 54 36 81 64 3 11 14 196 -3 -3 9 9 9 154 4 13 23 529 -1 6 -6 1 36 299 5 16 18 324 2 1 2 4 1 288 6 16 21 441 2 4 8 4 16 336 7 20 19 361 6 2 12 36 4 380 8 22 22 484 8 5 40 64 25 484 Total 2520 167 218 208 2071 | View Page |
| Root Cause Analysis JCAHO has a very important role in cause analysis of medical errors. Since 1995, it has been increasing its focus on patient safety by requiring in-depth analysis, Root Cause Analysis, to determine the underlying causes of every adverse event.
Root causes are specific reasons that contribute to medical errors. They cause mistakes that lead to great patient harm (adverse events). Usually they can be identified.
Examples:
Using a wrong calculation factor
Neglecting to use directions for complicated tests
Reporting the wrong test result
Using outdated reagents
Testing clotted or partially-filled samplesDiluting a test sample incorrectly
In most cases, management has the authority and means to resolve root causes. Root Cause Analysis also recommends actions to prevent reoccurrence of an adverse event. | View Page |
| Specificity Example: Calculations (1) Determining the specificity of the experimental method will help show if the test is worthwhile.Using the equation for specificity, we insert the following numbers:
100 True Negatives
Divided by (100 True Negatives + 275 False Positives)
Times 100
or
(100 ÷ (100 + 275)) x 100. The specificity for the “Experimental” method is 26.6%.
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| Sensitivity Example: Calculations (1) Let's return to our experiment method from the previous example. This time, we'll calculate the sensitivity.The experimental method produced 600 true positives and 25 false negatives. By inserting these numbers into the sensitivity equation, we get (600 + (600 + 25)) x 100.Thus, the experimental method has a sensitivity of 96%. | View Page |
| Sensitivity Example: Calculations (2) The tried-and-true method also had 600 true positives but had 50 false negatives. Once again inserting these numbers into our equation, (600 ÷ (600 + 50)) x 100, we find that the tried-and-true method has a sensitivity of 92%. | View Page |
| CUSUM Example: Plotting Control Data To illustrate the use of CUSUM in the laboratory, we'll use daily control values for glucose testing.First, we'll list daily control values under "daily results."
Then, we'll calculate mean by using formula A.
Next, we can find the difference from the mean for each result, and square that result for the two relevant columns.
Using all of the squared differences from the mean, we can find the standard deviation using formula B.
Using the mean from formula A and the standard deviation calculations from formulas B and C, we can plot our data points on the Levey-Jennings chart.
Formula D helps us calculate the coefficient of variation (CV), which expresses SD as a percentage of mean value and is more reliable for comparing precision at different concentration levels. The lower the CV the greater the precision.
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| Specificity Example: Calculations (2) To calculate the specificity of the tried-and-true method, we'll use these numbers:
325 True Negatives
Divided by (325 True Negatives + 25 False Positives)
Times 100
or
(325 ÷ (325 + 25)) x 100. The specificity for the tried-and-true method is 92.8%.
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| What is internal quality control? | View Page |
| Prerequisites The basic laboratory skills that you will need to do a semen analysis include:
Using a microscopePerforming manual cell counts and doing calculations to determine the concentration of those cells per milliliter of fluidMeasuring volumeMeasuring pHMeasuring viabilityKnowledge of OSHA regulations for handling potentially infectious human fluids | View Page |
| High viscosity If the specimen is more viscous than normal, it may be difficult to dilute it or to load it onto counting chambers in the undiluted condition. In this rare situation the semen may need to be manipulated to reduce the viscosity before a count is done. One method to do this is to repeatedly pipet the specimen up and down with an equal volume of culture medium. Care must be taken to avoid foaming. Other methods include enzyme digestion, for example with bromelain at a concentration of 1 gm / liter, or addition of a small amount of emulsifier, such as Alevare or chymotrypsin.
Any manipulation of this type must be recorded on the report sheet. Calculation of the number of sperm per milliliter will also have to be corrected for any dilution. | View Page |