Crush Step 1 E-Book (eBook)

Crush Step 1 E-Book

Thomas E. Blair, Theodore X. O'Connell, Ryan A. Pedigo (Autoren)

eBook Download: EPUB

2023 | 3. Auflage
720 Seiten
Elsevier Health Sciences (Verlag)
978-0-323-87919-4 (ISBN)

Up-to-date, easy-to-read, high-yield coverage of all the material tested on the exam-everything from biostatistics, microbiology, and pharmacology to immunology, oncology, psychiatry, and more.
Numerous color images (many are new), helpful lists, and quick-reference tables help you retain and recall information quickly.
Review questions for each chapter test your mastery of core knowledge and aid retention of high-yield facts.
Test prep strategies help you identify and understand question stems rather than simply memorizing buzz words.
A new review board of current students and residents, as well as authors/reviewers who scored in the 99th percentile on the USMLE Step 1, ensures that content is current, relevant, and accurate from cover to cover.

Dr. Ted O'Connell is a family physician, educator, author, innovator, and speaker. He served as the founding director of Kaiser Permanente's Napa-Solano family medicine residency program and serves as a clinical professor for UC San Francisco, UC Davis, and Drexel University's schools of medicine. He also founded Kaiser's community medicine and global health fellowship. has authored or edited over a dozen textbooks, written numerous textbook chapters and peer-reviewed journal articles. Among his most notable are USMLE Step 2 Secrets, USMLE Step 3 Secrets, Crush Step 1, and the Instant Workups series of books. He is the Editor-In-Chief for Elsevier's Clinical Key Meded education platform.

Written and reviewed by students, residents, and experts, and led by bestselling review author Dr. Ted O'Connell, Crush Step 1, 3rd Edition, is the perfect review resource you need to pass this high-stakes exam. Now extensively revised and updated to support your coursework and exam preparation, this comprehensive, focused resource is the most effective review tool available for truly understanding the material on which you'll be tested. - Up-to-date, easy-to-read, high-yield coverage of all the material tested on the exam everything from biostatistics, microbiology, and pharmacology to immunology, oncology, psychiatry, and more. - Numerous color images (many are new!), helpful lists, and quick-reference tables help you retain and recall information quickly. - Review questions for each chapter test your mastery of core knowledge and aid retention of high-yield facts. - Test prep strategies help you identify and understand question stems rather than simply memorizing buzz words. - A new review board of current students and residents, as well as authors/reviewers who scored in the 99th percentile on the USMLE Step 1, ensures that content is current, relevant, and accurate from cover to cover.

1: Biostatistics

Thomas E. Blair, Kian Preston-Suni

Mean, median, and mode

❍ Sample value set: 1, 1, 2, 4, 5, 7, 7, 25, where n = 8
❍ Mean: The average of a sample. It is calculated by adding all values, then dividing by the number of values (n). In the sample set just given, (1 + 1 + 2 + 4 + 5 + 7 + 7 + 25)/8 = 6.5. The mean is sensitive to extreme values.
❍ Median: The middle value of a sample. It is equivalent to the 50th percentile such that half the sample values are above and half are below. It is identified by arranging the values in ascending order, then finding the middle-most number. If n is odd, the median is the [(n + 1)/2]th largest observation. If n is even, the median is the average of the (n/2)th and the (n/2 + 1)th largest observations. In this example, there is an even number of values, so the median is the average of the two middle-most numbers, that is, for 1, 1, 2, 4, 5, 7, 7, 25, the median = (4 + 5)/2 = 4.5. An advantage of the median is that it is not sensitive to extreme values. You may notice that in this sample the mean is greater than the median. This indicates that the distribution has a positive skew (see later discussion).
❍ Mode: The most frequently occurring value in a sample. In this example, both 1 and 7 are modes because they both appear twice. Therefore, this data set can be said to be bimodal.

Mean: average value
Median: middle value
Mode: most frequent value

❍ Standard deviation (SD): A measure of the spread and variability of a data set, calculated as the square root of the variance. It represents the average deviation from the mean. The closer the values remain to the mean, the smaller the SD (Fig. 1.1). The concept, not the mathematics, may be tested on Step 1.
- ● Example: Normal body temperature will have a small SD because an individual’s anterior and posterior hypothalamus maintains temperature homeostasis within a very limited range. Blood sugars, on the other hand, will have a larger SD because glycemic loads change throughout the day.

Standard deviation represents the average deviation from the mean.

Fig. 1.1Data sets with the same mean but different standard deviations. The wider curve has a larger standard deviation than the taller curve.

Definitions

❍ Normal distribution: This is also known as a Gaussian distribution or bell-shaped curve. A probability function in which values are symmetrically distributed around a central value, and the mean, median, and mode are equal. In a normal distribution, 1 SD accounts for 68% of all values, 2 SDs account for 95% of all values, and 3 SDs account for 99.7% of all values—the 68-95-99 rule (Fig. 1.2). The area under the curve (AUC) is 1 (100%).
- ● Example: The intelligence quotient (IQ) test is constructed to follow a normal distribution with a mean of 100 and SD of 15. That means 95% of the population (2 SDs) will have an IQ between 70 and 130. Of clinical importance, intellectual disability is defined as an IQ of <70.

In a normal distribution, mean = median = mode.

❍ Bimodal distribution: A distribution with two modes.
- ● Example: The incidence of Crohn disease displays a bimodal distribution with the first peak between 15 and 30 years of age and the second peak between 60 and 80 years of age.
❍ Negative skew: An asymmetric distribution in which a tail on the left indicates that mean < median < mode. The tail is due to outliers on the left side of the curve.
- ● Example: A graphic representation of age at death would show a negative skew, with most people clustered at the right end of the distribution and relatively few dying at a younger age (Fig. 1.3A).
❍ Positive skew: An asymmetric distribution in which a tail on the right side indicates that mean > median > mode. The tail is due to outliers on the right side of the curve.
- ● Example: A graphic representation of age at initiation of smoking would display positive skew. Most people would be clustered around their late teens, but a small number of middle-aged and older adults, who initiated smoking later in life, create a positive tail (Fig. 1.3B).

Negative skew: tail on left
Positive skew: tail on right

Fig. 1.2In a normal distribution, 1 standard deviation (SD) accounts for 68% of all values, 2 SDs account for 95% of all values, and 3 SDs account for 99% of all values. Source: (From Marshall WJ, Bangert SK. Clinical Chemistry. 6th ed. Philadelphia: Elsevier; 2008.)

Fig. 1.3(A) Negative skew. (B) Positive skew. Source: (From Jekel JF, Katz DL, Wild JG, Elmore DMG. Epidemiology, Biostatistics, and Preventive Medicine. 3rd ed. Philadelphia: Elsevier; 2007.)

Epidemiology

❍ Incidence: The number of new cases of a disease in a population over a specific period (longitudinal).
❍ Prevalence: The total number of people in a population affected by a condition at one point (cross-sectional).

Incidence and prevalence are measures of morbidity

❍ Duration relates incidence to prevalence.
- ● Example: Upper respiratory infections (URIs) have a high annual incidence, occurring often during winter months, but a comparatively low prevalence, because most URIs resolve quickly. Diabetes has a comparatively low incidence but high prevalence, because a patient who has diabetes generally has it for life.
❍ Rates report the total number of changes in a particular health status among a population per unit of time.
- ● Example: The crude mortality rate is the total number of deaths recorded in a given period of time divided by the total population, usually reported as deaths per 1,000 or deaths per 100,000 population.
- ● Maternal mortality rate: Number of deaths related to pregnancy divided by live births during the same time period (reported as cases per 100,000).
- ● Infant mortality rate: Total deaths among children younger than 1 year divided by number of live births during the same time period (reported as cases per 1,000).
- ● Age-adjusted (age-standardized) mortality rate: Accounts for differences in population age distributions to allow for comparisons between populations. Death rates differ by age; older populations generally have higher death rates.
  - ● Example: Death due to coronary artery disease is more common among older people. Without adjusting for mortality by age, a young country would appear to have a lower death rate than a middle-age or old country. Standardizing by age allows for comparisons between these two countries.
❍ Life expectancy: The average age a member of a population expects to achieve, at a given age. This is often reported as life expectancy at birth.
- ● Example: In 2020 a 60-year-old could expect to live another 22.9 years, on average. This is longer than the life expectancy at birth of 77.8 years, because this average includes some people who die before the age of 60 years.
❍ Health-adjusted life expectancy: The average time a person of a certain age with given health conditions can expect to live.
- ● Example: A 50-year-old with diabetes and coronary artery disease has a lower life expectancy than a healthy 50-year-old.
❍ Years of potential life lost: A measure of how long a person would have lived if they had not died of a particular condition.
- ● Example: Motor vehicle accidents cause more years of potential life lost than strokes, because on average people who die of strokes are older.
❍ Quality-adjusted life year: A measure used to assess the value of an intervention. A year of productive life in perfect health is more valuable than a year spent bedridden and in pain.
❍ Disability-adjusted life year: This measure combines both years of productive life lost and disability due to disease, capturing the effects of both morbidity and mortality on a population.
❍ Standardized mortality ratio (SMR): This metric relates the observed deaths in a population to the rate of deaths that would be expected without the exposure of interest.
- ● Example: A study shows that a group of people who exercise frequently have 10% fewer deaths due to heart disease...

Erscheint lt. Verlag	8.1.2023
Sprache	englisch
Themenwelt	Medizin / Pharmazie ► Allgemeines / Lexika
Themenwelt	Medizin / Pharmazie ► Studium
ISBN-10	0-323-87919-5 / 0323879195
ISBN-13	978-0-323-87919-4 / 9780323879194

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