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# Primary syllabus: Physics and statistics

Created: 7/9/2004
Updated: 7/3/2007

 Trainees should understand the physical principles upon which methods of clinical measurement are based. Knowledge of clinical measurement techniques should be limited to principles and basic methods. Mathematical concepts: sinusoids, exponentials and parabolas. Exponential functions and logarithms Basic measurement concepts: linearity, drift, hysteresis, signal:noise ratio, dynamic response SI units. Fundamental and derived units Simple mechanics: mass, force, work and power Heat: simple calorimetry. Conduction, convection, radiation. Mechanical equivalent of heat: laws of thermodynamics Basic concepts of electricity and magnetism. Capacitance, inductance and impedance. Amplifiers. Band width, filters. Amplification of biological potentials: ECG, EMG, EEG. Sources of electrical interference Processing, storage and display of physiological measurements. Bridge circuits Basic principles of lasers Principles of cardiac pacemakers and defibrillators Electrical hazards: causes and prevention. Electrocution, fires and explosions. Diathermy and its safe use Principles of pressure transducers. Resonance and damping, frequency response Measurement of pressure. Direct and indirect methods of blood pressure measurement. Pulmonary artery pressure Measurement of volume and flow in gases and liquids. The pneumotachograph and other respirometers. Peak flow measurement. Spirometry. Cardiac output Measurement of temperature and humidity Measurement of gas concentrations, especially oxygen, carbon dioxide, nitrogen, nitrous oxide, volatile anaesthetic agents Measurement of pH, pCO2, pO2 Simple tests of pulmonary function Capnography Pulse oximetry Measurement of pain Candidates will be required to demonstrate understanding of basic statistical concepts, but will not be expected to have practical experience of statistical methods. Emphasis will be placed on methods by which data may be summarised and presented, and on the selection of statistical measures for different data types. Candidates will be expected to understand the statistical background to measurement error and statistical uncertainty. Descriptive statistics Categories of data. Statistical distributions (Gaussian, chi-squared, binomial) and their parameters. Non-parametric measures of location and variability. Graphical presentation of data Deductive and inferential statistics Simple probability theory. Confidence intervals. Linear regression. Linear correlation The null hypothesis. Type I and type II errors. Probability of error occurrence, and the power of a test to detect a significant difference, Bland-Altman plot. Choice of simple statistical tests for different data types ArticleDate:20040907 SiteSection: Article