archer theory notes - 925jerk

[ArchivalUser]

i purchased archer theory they said they will mail me the notes but i did not get it .so anyone please tell me how to get it .i can only access the online part .thanks in advance

[ArchivalUser]

I think you need to email them. They are not the most regular in their communications.
I purchased several of the Review materials, and I got only one or two of their powerpoint material, that I was supposed to get.
The content is really great though. I'd still recommend it.
Good luck

[ArchivalUser]

thanks for reply i will do as u said

[ArchivalUser]

@ vesodoc
i need some advice also about mcq's .tell me how to do well, as step 3 is vague .i am comfortable with ccs .i have seen a lot of people fail even they do well in step1 and 2 .i am reading archer ,mtb,uworld for theory and ccs .is it ok or any anything else please let me know.

[ArchivalUser]

You know, I still wait for the result tonight, there's a high chance for me failing (again) Step 3, so I don't know how relevant my advice would be.
But anyway,
I'd focus on limited sources. MCQs of UW, Kaplan are more then enough, if you do them (I suggest both), twice. Look, Step 3 is a cumulative exam, so the more you do MCQs, the better. These 2 banks would be enough.

Additionally - bulk texts of Archer notes, MTB s, you might use the Kaplan notes for Step 3 ( the 3 yellow books), in their case studies part, if you have time.

You should be good though even only with both banks (twice) and MTB.
PLUS: (very important!) CCS Workshops Archer. It's downloadable, 88 $, a week is enough. You do also the UW CCS, as many as you can.

Now, I guess we'll need to emphasize on statistics too. Even more so after November.
Kaplan will come out with a new MTB book. I guess the rest will remain unchanged.

Good luck!

[ArchivalUser]

vesodoc and 925jerk,

could you please advice me what material of Archer I should buy?

[ArchivalUser]

Manors, archer CCS workshop videos and theory lecture videos are enough along with uw

[ArchivalUser]

I agree with gmason.

@ manita, for archers buy $500 one, so that you can listen to all the theory lectures at least twice.
Regarding archer ccs, people most commonly buy $ 88 one, which is the recordings of four ccs live webinars. I used archers lectures and ccs videos in addition to UW mcqs and ccs. They helped me alot and I passed the exam despite messing up on atleast 4 ccs cases. In addition to Archer and UW, I found few very helpful links on the forum.
http://www.cram.com/flashcards/usmle-step-3-ccs-4977633 (for repetitive ccs practice on USMLE software)

Derma: http://www.cram.com/flashcards/usmle-3-dermatology-4621367
For Biostat I found these notes helpful.

Biostatistics
Get in the habit of drawing a 2 × 2 table to make calculations easier. Be sure to know how to calculate the common biostatistic parameters listed in Figure 1-1. On the boards, watch out for columns or rows being switched around.
FIGURE1-1 
Biostatistic parameters. NPV, Negative predictive value; PPV, positive predictive value.
(From Mandell G, Bennett J, Dolin R: Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Disease, 7th ed. Philadelphia, Churchill Livingstone, 2009.)
Sensitivity: Ability to detect disease; mathematically, the number of true positives divided by the number of people with the disease. Tests with high sensitivity are used forscreening; they may have false-positive results but do not miss many people with the disease (low false-negative rate).
Specificity: Ability to detect health (nondisease); mathematically, the number of true negatives divided by the number of people without the disease. Tests with high specificity are used for disease confirmation; they may yield false-negative results but do not label as sick anyone who is actually healthy (low false-positive rate). The ideal confirmatory test must have high sensitivity and high specificity. Otherwise, people with the disease may be called healthy.
CASE SCENARIO
A researcher says that the cutoff fasting glucose value for the diagnosis of diabetes should be lowered from 126 mg/dL to 110 mg/dL. How would this change affect the test’s number of false-negative and false-positive results? Fewer false negatives, more false positives. If the cutoff value is raised, fewer people will be called diabetic (more false negatives, fewer false positives).
Positive predictive value (PPV): when a test is positive for disease, the PPV measures how likely it is that the patient has the disease (probability of having a condition, given a positive test). Mathematically, the number of true positives is divided by the number of people with a positive test. PPV depends on the prevalence of the disease and the sensitivity/specificity of the test (e.g., an overly sensitive test that gives more false positives has a lower PPV).
CASE SCENARIO
How does prevalence affect the PPV? The higher the prevalence, the greater the PPV. See Figure 1-2.
FIGURE1-2 
Positive predictive value (PPV) of a human immunodeficiency virus (HIV) confirmatory assay. The PPV is a function of disease prevalence. At very low disease prevalence (0.01%), the PPV of testing declines sharply with any false-positive test results. As the prevalence of infection increases, the PPV improves. HIV screening eliminates a majority of HIV-negative individuals for further confirmation, and the prevalence of the population of samples referred for confirmatory assay increases dramatically and will be in the range of 90%. The PPV for the sequential strategy remains high despite any false-positive results.
(From Mandell G, Bennett J, Dolin R: Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Disease, 7th ed. Philadelphia, Churchill Livingstone, 2009.)
Negative predictive value (NPV): When a test is negative for disease, the NPV measures how likely it is that the patient is healthy (probability of not having a condition, given a negative test). Mathematically, the number of true negatives is divided by the number of people with a negative test. Like PPV, NPV depends on the prevalence of the disease and the sensitivity/specificity of the test (the higher the prevalence, the lower the NPV).
CASE SCENARIO
How does sensitivity affect NPV? The more sensitive the test, the fewer the number of false negatives and the higher the NPV.
Attributable risk: Number of cases attributable to one risk factor (put another way, the amount you would expect the incidence to decrease if a risk factor were removed). For example, if the incidence rate of lung cancer in the general population is 1/100 and in smokers it is 10/100, the attributable risk of smoking in causing lung cancer is 9/100, assuming a properly matched control (i.e., 10/100 − 1/100 = 9/100).
Relative risk: Compares the disease risk in the exposed population to the disease risk in the unexposed population. Relative risk can be calculated only after a prospective or experimental study. Any value for relative risk other than 1 is clinically significant. For example, if the relative risk is 2.0, a person is twice as likely to develop the condition if exposed to the factor in question. If the relative risk is 0.5, the person is only half as likely to develop the condition when exposed to the factor; in other words, the factor protects the person from developing the disease.
CASE SCENARIO
After completing a chart and autopsy record review, a researcher finds that pancreatic cancer occurred in 5/1000 smokers and 1/1000 nonsmokers. What is the relative risk of pancreatic cancer in smokers? The relative risk cannot be calculated because this is a retrospective study. Choose “none of the above/can’t be calculated.”
Odds ratio: Used only for retrospective studies (e.g., case-control). The odds ratio compares the odds of having disease versus not having disease in exposed populations versus the odds of having disease versus not having disease in unexposed populations. There should be more disease in exposed than unexposed populations and more nondisease in unexposed than exposed populations. The odds ratio is a less-than-perfect way to estimate relative risk from retrospective data.
CASE SCENARIO
A retrospective study finds that pancreatic cancer occurred in 5/1000 smokers and 1/1000 nonsmokers. What is the odds ratio for pancreatic cancer in smokers? 5/995 divided by 1/999, or 5.02.
Standard deviation (SD): With a normal or bell-shaped distribution, 1 SD holds 68% of values, 2 SDs hold 95% of values, and 3 SDs hold 99.7% of values. In a normal distribution, the mean = median = mode (mean is the average value, median is the middle value, and mode is the most common value).
CASE SCENARIO
A child scores 140 on an IQ test. A review of the literature reveals that the mean IQ in the child’s community is 100, with an SD of 20. How does the child’s score compare with that of other children? The child did better on the examination than 97.5% of children in the community. The child scored 2 standard deviations above the mean, which holds 95% of the values. Because 2.5% fall on each end of the bell-shaped curve, the child did better on the examination than 97.5% of children in the community.
Skewed distribution: A positive skew is asymmetry with an excess of high values (tail on right, mean > median > mode); a negative skew is asymmetry with an excess of low values (tail on left, mean < median < mode). Because positive and negative skews (Fig. 1-3) are not normal distributions, standard deviation and mean are less meaningful values.
FIGURE1-3 
Group A demonstrates a negative skew (tail on the left), group B has a normal distribution, and group C has a positive skew (tail on the right).
Test reliability (synonymous with precision) measures the reproducibility and consistency of a test (e.g., the concept of interrater reliability: if two different people administer the same test, the examinee will get the same score if the test is reliable).Random error reduces reliability/precision (e.g., limitation in significant figures).
Test validity (synonymous with accuracy) measures the trueness of measurement—whether the test measures what it claims to measure (e.g., if you give a valid IQ test to a genius, the test should not indicate that person has a mental disability). Systematic error reduces validity/accuracy (e.g., miscalibrated equipment).
Correlation coefficient measures how related two values are. The range of the coefficient is −1 to +1. The important point in determining the strength of the relationship between two variables is how far the number is from zero (i.e., absolute value). Zero equals no association whatsoever, +1 equals a perfect positive correlation (when one goes up, so does the other), and −1 equals a perfect negative correlation (Fig. 1-4).
FIGURE1-4 
Correlation graphs.
CASE SCENARIO
Which is a stronger correlation, +0.3 or −0.3? They are equal.
Confidence interval: When you take a set of data and calculate a mean, you want to say that the result is equivalent to the mean of the whole population, but usually the two values are not exactly equal. The confidence interval (usually set at 95%) says that you are 95% confident that the mean of the population is within a certain range (generally within 2 SD of your experimental or derived mean using an adjustment for the sample size). A confidence interval (confidence limits) expressed as 76 < X < 84 = 0.95 means that you are 95% certain that the mean for the whole population (X) is between 76 and 84.
Different types of studies (listed in decreasing order of quality and desirability)
1. 1.
Experimental study/randomized controlled trial: the gold standard type of study. Compares two equal groups in which one variable is manipulated and its effect is measured. Remember to check for double-blinding (or at least single-blinding) and well-matched control subjects.
2. 2.
Prospective/longitudinal/cohort/incidence/follow-up study: choose a sample population, divide it into two groups on the basis of the presence or absence of a risk factor, and follow the groups over time to see what diseases they develop. This approach is sometimes called an observational study because all you do is observe. For example, you may follow people with and without asymptomatic hypercholesterolemia to determine whether people with hypercholesterolemia have a higher incidence of myocardial infarction later in life. You can calculate relative risk and incidence. This type of study is time-consuming, expensive, and good for common diseases, whereas retrospective studies are less expensive, less time-consuming, and good for rare diseases.
3. 3.
Retrospective/case-control study: samples are chosen after the fact on the basis of the presence (cases) or absence (controls) of disease. Then information can be collected about risk factors. For example, you may look at people with lung cancer versus people without lung cancer to determine if the people with lung cancer smoke more.
4. 4.
Case series: good for extremely rare diseases. You simply describe the clinical presentation of people with a certain disease. Case series may suggest the need for a retrospective study.
5. 5.
Prevalence survey/cross-sectional survey: looks at the prevalence of a disease and prevalence of risk factors. When two different cultures are compared, you may get an idea for the cause of a disease, which can be tested with a prospective study (e.g., more colon cancer and higher-fat diet in the United States versus less colon cancer and low-fat diet in Japan).
Incidence: the number of new cases of disease in a unit of time (generally in 1 year, but any time frame can be used). The incidence rate is equal to the absolute risk (as opposed to relative or attributable risk). In an epidemic, the observed incidence greatly exceeds the expected incidence.
Prevalence: the total number of cases of disease (new or old).
CASE SCENARIO
If a widely used new form of chemotherapy allows patients with lung cancer to survive an extra 2 to 3 years without curing the disease, what will happen to the incidence and prevalence of lung cancer? Nothing happens to the incidence, but the prevalence will increase because people live longer.
CASE SCENARIO
For influenza, which is higher—incidence or prevalence? In short-term diseases (like the flu), the incidence is often higher than the prevalence (opposite of chronic diseases).
Comparison of data
1. 1.
Chi-squared test: used to compare percentages or proportions (nonnumeric or nominal data)
2. 2.
T-test: used to compare two means
3. 3.
Analysis of variance (ANOVA): used to compare three or more means
P value: If someone gives you data and tells you that P