Giuseppina CERSOSIMO | C.I. BEHAVIORAL SCIENCES AND SCIENTIFIC METHODOLOGY 2

cod. 1060200002

C.I. BEHAVIORAL SCIENCES AND SCIENTIFIC METHODOLOGY 2

	1060200002
	DEPARTMENT OF MEDICINE, SURGERY AND DENTISTRY "SALERNITANA MEDICAL SCHOOL"
	EQF7
	DENTISTRY
	2024/2025

PERCORSO COMUNE

	OBBLIGATORIO
	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2023
	SPRING SEMESTER

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
1	C.I. SCIENZE COMPORTAMENTALI E METODOLOGIA SCIENTIFICA 2 - MOD. ODONTOIATRIA PREVENTIVA E DI COMUNITÀ
	MED/28	2	22	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS
2	C.I. SCIENZE COMPORTAMENTALI E METODOLOGIA SCIENTIFICA 2 - MOD. SOCIOLOGIA GENERALE
	SPS/07	1	11	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS
3	C.I. SCIENZE COMPORTAMENTALI E METODOLOGIA SCIENTIFICA 2 - MOD. STATISTICA MEDICA
	MED/01	4	44	LESSONS	BASIC COMPULSORY SUBJECTS

PROFESSORS

	FABRIZIO BARONE3 T Curriculum
	-31
	GIUSEPPINA CERSOSIMO2 Curriculum

	Objectives
	MODULE OF MEDICAL STATISTICS: THE STUDENT WILL ACQUIRE KNOWLEDGE OF THE MAIN ISSUES OF MEDICAL STATISTICS OF INTEREST TO THE DEGREE COURSE. IN PARTICULAR, HE WILL ACQUIRE KNOWLEDGE OF THE MAIN MODELS AND THEOREMS OF MEDICAL STATISTICS, IN CONNECTION WITH THE ASSUMPTIONS ON WHICH THESE MODELS ARE BASED, CRITICALLY UNDERSTANDING ALSO LIMITS OF VALIDITY AND APPLICABILITY IN REAL CONTEXTS (KNOWLEDGE AND UNDERSTANDING). THE STUDENT WILL BE ABLE TO SUCCESSFULLY APPLY THE MODELS AND THEOREMS OF MEDICAL STATISTICS TO QUALITATIVE AND QUANTITATIVE DESCRIPTION OF REAL CASES ALSO, AND ESPECIALLY, IN INTERDISCIPLINARY CONTEXTS OF INTEREST TO THE DEGREE COURSE, BY TESTING HYPOTHESES AND CONDITIONS ON WHICH THESE MODELS AND THEOREMS ARE BASED. THE STUDENT WILL ALSO BE ABLE TO EVALUATE THE QUALITY OF THE EXPECTED RESULTS BOTH IN RELATION TO THE INTRINSIC APPROXIMATIONS OF THE MODELS AND THEOREMS ADOPTED AND IN RELATION TO THE MEASUREMENT ERRORS (APPLIED KNOWLEDGE AND UNDERSTANDING). THE STUDENT WILL BE ABLE TO INTEGRATE THE NECESSARY KNOWLEDGE AND MANAGE THE COMPLEXITY OF THE INFORMATION IN THE ACQUISITION AND APPLICATION OF THE KNOWLEDGE OF MEDICAL STATISTICS, TO FORMULATE JUDGMENTS AND ASSUMPTIONS OF WORK, ALSO ON THE BASIS OF INCOMPLETE OR LIMITED INFORMATION, AND ON THE EFFECTS AND RESPONSIBILITIES RELATED TO THE PRACTICAL APPLICATION OF THE KNOWLEDGE (INDEPENDENT JUDGMENT). THE STUDENT WILL ACQUIRE THE ABILITY TO PRESENT TOPICS COVERED IN THE COURSE OF MEDICAL STATISTICS WITH PROPERTIES AND SIMPLICITY OF LANGUAGE, AIMED ALSO TO A CLEAR AND EFFECTIVE TRANSMISSION OF CONTENTS AND RESULTS IN INTERDISCIPLINARY CONTEXTS, IN PRESENCE OF INTERLOCUTORS WHO ARE NOT EXPERTS IN THE FIELD (COMMUNICATION). THE STUDENT WILL ACQUIRE SKILLS ALLOWING HIM TO EXPAND AND DEEPEN THE THEMES OF MEDICAL STATISTICS AND ITS INTERDISCIPLINARY APPLICATIONS IN AN AUTONOMOUS WAY (LEARNING SKILLS). MODULE OF PREVENTIVE AND COMMUNITY DENTISTRY: DEMONSTRATE PROFICIENCY IN UNDERSTANDING CLASSICAL AND ADVANCED CONCEPTS IN COMMUNITY AND PREVENTIVE DENTISTRY- LEARN ABILITY TO USE EPIDEMIOLOGICAL AND STATISTICAL METHODS AND STRATEGY - LEARN ABILITY TO INTEGRATE EPIDEMIOLOGICAL INFORMATIONS WITH CLINICAL JUDGEMENT- LEARN TO UNDERSTAND THE SCIENTIFIC METHODOLOGY INCLUDING THE ABILITY TO EVALUATE DATA ANALYSIS AND SCIENTIFIC LITERATURE AND INTEGRATE THE INFORMATIONS TO SOLVE CLINICAL PROBLEMS – ABILITY TO DEVELOP DENTAL PREVENTIVE AND TREATMENT PLANS FOR THE COMMUNITY THE COURSE AIMS TO TEACH STUDENTS THE KNOWLEDGE, SKILLS AND ABILITIES FOR CRITICAL REFLECTION WITH RESPECT TO THE USE OF RESEARCH.

MODULE OF MEDICAL STATISTICS:
THE STUDENT WILL ACQUIRE KNOWLEDGE OF THE MAIN ISSUES OF MEDICAL STATISTICS OF INTEREST TO THE DEGREE COURSE. IN PARTICULAR, HE WILL ACQUIRE KNOWLEDGE OF THE MAIN MODELS AND THEOREMS OF MEDICAL STATISTICS, IN CONNECTION WITH THE ASSUMPTIONS ON WHICH THESE MODELS ARE BASED, CRITICALLY UNDERSTANDING ALSO LIMITS OF VALIDITY AND APPLICABILITY IN REAL CONTEXTS (KNOWLEDGE AND UNDERSTANDING).
THE STUDENT WILL BE ABLE TO SUCCESSFULLY APPLY THE MODELS AND THEOREMS OF MEDICAL STATISTICS TO QUALITATIVE AND QUANTITATIVE DESCRIPTION OF REAL CASES ALSO, AND ESPECIALLY, IN INTERDISCIPLINARY CONTEXTS OF INTEREST TO THE DEGREE COURSE, BY TESTING HYPOTHESES AND CONDITIONS ON WHICH THESE MODELS AND THEOREMS ARE BASED. THE STUDENT WILL ALSO BE ABLE TO EVALUATE THE QUALITY OF THE EXPECTED RESULTS BOTH IN RELATION TO THE INTRINSIC APPROXIMATIONS OF THE MODELS AND THEOREMS ADOPTED AND IN RELATION TO THE MEASUREMENT ERRORS (APPLIED KNOWLEDGE AND UNDERSTANDING).
THE STUDENT WILL BE ABLE TO INTEGRATE THE NECESSARY KNOWLEDGE AND MANAGE THE COMPLEXITY OF THE INFORMATION IN THE ACQUISITION AND APPLICATION OF THE KNOWLEDGE OF MEDICAL STATISTICS, TO FORMULATE JUDGMENTS AND ASSUMPTIONS OF WORK, ALSO ON THE BASIS OF INCOMPLETE OR LIMITED INFORMATION, AND ON THE EFFECTS AND RESPONSIBILITIES RELATED TO THE PRACTICAL APPLICATION OF THE KNOWLEDGE (INDEPENDENT JUDGMENT).
THE STUDENT WILL ACQUIRE THE ABILITY TO PRESENT TOPICS COVERED IN THE COURSE OF MEDICAL STATISTICS WITH PROPERTIES AND SIMPLICITY OF LANGUAGE, AIMED ALSO TO A CLEAR AND EFFECTIVE TRANSMISSION OF CONTENTS AND RESULTS IN INTERDISCIPLINARY CONTEXTS, IN PRESENCE OF INTERLOCUTORS WHO ARE NOT EXPERTS IN THE FIELD (COMMUNICATION).
THE STUDENT WILL ACQUIRE SKILLS ALLOWING HIM TO EXPAND AND DEEPEN THE THEMES OF MEDICAL STATISTICS AND ITS INTERDISCIPLINARY APPLICATIONS IN AN AUTONOMOUS WAY (LEARNING SKILLS).

MODULE OF PREVENTIVE AND COMMUNITY DENTISTRY:
DEMONSTRATE PROFICIENCY IN UNDERSTANDING CLASSICAL AND ADVANCED CONCEPTS IN COMMUNITY AND PREVENTIVE DENTISTRY- LEARN ABILITY TO USE EPIDEMIOLOGICAL AND STATISTICAL METHODS AND STRATEGY - LEARN ABILITY TO INTEGRATE EPIDEMIOLOGICAL INFORMATIONS WITH CLINICAL JUDGEMENT- LEARN TO UNDERSTAND THE SCIENTIFIC METHODOLOGY INCLUDING THE ABILITY TO EVALUATE DATA ANALYSIS AND SCIENTIFIC LITERATURE AND INTEGRATE THE INFORMATIONS TO SOLVE CLINICAL PROBLEMS – ABILITY TO DEVELOP DENTAL PREVENTIVE AND TREATMENT PLANS FOR THE COMMUNITY

THE COURSE AIMS TO TEACH STUDENTS THE KNOWLEDGE, SKILLS AND ABILITIES FOR CRITICAL REFLECTION WITH RESPECT TO THE USE OF RESEARCH.

	Prerequisites
	MODULE OF MEDICAL STATISTICS:BASIC KNOWLEDGE OF MATHEMATICS FOR THE DENTAL DEGREE COURSE : ACCORDING TO SECTION 11 OF THE PUBLISHED DIDACTIC GUIDELINES

	Contents
	MEDICAL STATISTICS MODULE COMMON/SHARED SYLLABUS INTRODUCTION TO THE STUDY OF STATISTICS AND PROBABILITY IN THEIR HISTORICAL CONTEXT. USEFULNESS OF KNOWLEDGE ON STATISTICS AND PROBABILITY IN THE CLINICAL FIELD. PROBABILITY. BASIC DEFINITIONS (EVENT E; NEGATIVE OR COMPLEMENT; UNION, INTERSECTION AND IMPLICATION; INCOMPATIBLE EVENTS AND NECESSARY EVENTS; PARTITION). PROBABILITY ACCORDING TO PASCAL, PROBABILITY OF THE CONTRARY EVENT. TOTAL PROBABILITY THEOREM (INCOMPATIBLE EVENTS, COMPATIBLE EVENTS AND COMPLEMENTARY EVENTS). COMPOUND PROBABILITY. EMPLOYEE AND INDEPENDENT EVENTS. TREE DIAGRAMS AND PROBABILITY CALCULATION ON TREE DIAGRAMS. INDEPENDENCE (DE MOIVRE) AND THEOREMS OF COMPOUND PROBABILITY (FOR INDEPENDENT EVENTS AND DEPENDENT EVENTS). PROBABILITY ACCORDING TO THE FREQUENTIST DEFINITION. EMPIRICAL LAW OF CASE. PROBABILITY ACCORDING TO KOLMOGOROV'S AXIOMATIC APPROACH, AXIOM OF PROBABILITY. BAYES THEOREM. COMBINATORY CALCULATION: ARRANGEMENTS, PERMUTATIONS AND COMBINATIONS. MEASUREMENT THEORY. RANDOM AND DETERMINISTIC VARIABLE. MEASUREMENT AND TRUE VALUE. MEASURING SCALES. FEATURES OF MEASURING INSTRUMENTS: PRECISION; ACCURACY; SENSITIVITY; RESOLUTION. PROBABILITY DISTRIBUTIONS: CUMULATIVE DISTRIBUTION FUNCTION; DISCRETE DENSITY FUNCTION AND ITS PROPERTIES; PROBABILITY DENSITY FUNCTION AND ITS PROPERTIES; MEAN, VARIANCE AND STANDARD DEVIATION; CHEBYSHEV INEQUALITY; DISCRETE, BINOMIAL, HYPERGEOMETRIC, POISSON, CONTINUOUS, GAUSSIAN DISTRIBUTION; PROPERTY OF THE GAUSSIAN DISTRIBUTION; STANDARDIZED NORMAL DISTRIBUTION; USE OF TABLES AND MATHEMATICAL PROPERTIES USEFUL FOR THEIR USE; DISTRIBUTION OF CHI SQUARE, F OF FISHER, T OF STUDENT. ERRORS: ABSOLUTE AND RELATIVE; AVERAGE VALUE AND TRUE VALUE; ACCURACY AND PRECISION; DEVIATIONS AND REJECTS; AVERAGE DEVIATION AND PERCENTAGE AVERAGE DEVIATION; LIMITS OF RELIANCE; VARIANCE AND STANDARD DEVIATION; SHAPE INDEXES (ASYMMETRY AND CURTOSIS). CENTRAL LIMIT THEOREM. CONFIDENCE INTERVALS. TYPES OF ASSOCIATION (CAUSAL; SPURIOUS; NON-CAUSAL). CAUSALITY CRITERIA (CONSISTENCE, STRENGTH, SPECIFICITY, TEMPORALITY, CONSISTENCY). SENSITIVITY, SPECIFICITY, FALSE POSITIVES AND FALSE NEGATIVES. POSITIVE AND NEGATIVE PREDICTIVITY. ESTIMATION OF SENSITIVITY AND SPECIFICITY. INTRODUCTION TO INFERENTIAL STATISTICS. CONFIDENCE INTERVALS: KNOWN SAMPLE AVERAGE (LARGE SAMPLES, UNKNOWN VARIANCE AND SMALL SAMPLE; DIFFERENCE BETWEEN AVERAGES, KNOWING THE POPULATION VARIANCIES AND LARGE SAMPLE OR IN THE CASE OF UNKNOWN VARIANCES); CONFIDENCE INTERVALS FOR POPULATION VARIANCE; ESTIMATION OF THE AVERAGE OF NON-NORMAL POPULATION (CASE OF BERNOULLIAN POPULATION); CONFIDENCE INTERVAL IN THE EVENT OF A DIFFERENCE BETWEEN PROPORTIONS. HYPOTHESIS TEST: HYPOTHESIS TEST; TYPES OF ERROR (FIRST AND SECOND TYPE); LEVEL OF SIGNIFICANCE AND POWER OF A TEST; P-VALUE; HYPOTHESIS TEST BASED ON TESTING THE MEAN (LARGE N OR GAUSSIAN DISTRIBUTION OF PARAMETER WITH KNOWN VARIANCE; SMALL N AND UNKNOWN VARIANCE; TYPICAL VALUES OF THE CRITICAL VALUE); CHECK ON THE SHAPE OF THE DISTRIBUTION (CHI-FRAME TEST OF ADAPTATION); PROPORTION HYPOTHESIS TEST; HYPOTHESIS TEST ON THE DIFFERENCE BETWEEN TWO MEANS (NORMAL DISTRIBUTIONS WITH KNOWN VARIANCES; UNKNOWN VARIANCES); T TEST FOR PAIRED DATA; TEST OF EQUALITY BETWEEN PROPORTIONS; CHI SQUARE TEST OF INDEPENDENCE AND CONTINGENCY TABLES; DEGREE OF ASSOCIATION; ODDS RATIO AND RISK RATIO AND CONFIDENCE INTERVALS FOR ODDS RATIO AND RISK RATIO (SIGNIFICANCE OF ASSOCIATION). ANALYSIS OF VARIANCE (ANOVA): GENERAL PRINCIPLES; ANOVA MODEL AND ASSUMPTIONS ON THE MODEL; HOMOSKEDASTICITY OF RANDOM VARIABLES; DEVIANCE (VERIFICATION BETWEEN GROUPS) AND RESIDUAL VARIATION WITHIN GROUPS; BASIC AND ALTERNATIVE PROCEDURE FOR ANOVA; POST HOC COMPARISONS; ORTHOGONAL COMPARISONS AND PROCEDURE; REPEAT TESTS; DATA MATRIX; MULTI-FACTOR MODEL; CALCULATION WITH EXPERIMENT 2 × 2; DEVIANCE BROKEN BETWEEN GROUPS; COMPARISONS; INTERACTING AND NON-INTERACTING FACTORS (GRAPHIC REPRESENTATION). ONLY FOR MEDICINE STUDENTS IN ADDITION TO THE ABOVE TOPICS: INTRODUCTION TO EVIDENCE BASED MEDICINE: THE EBM CYCLE, THE CLINICAL DEMAND (TYPES OF CLINICAL QUESTION; FINER CRITERION; PICO CRITERION); HIERARCHY OF EVIDENCE. TYPES OF STUDY (CASE SERIES, SECTIONAL STUDIES, COHORT, CASE-CONTROL, CLINICAL TRIAL). INCLUSION AND EXCLUSION CRITERIA. BASIC DEFINITIONS: TYPES OF EVENT (EQUIPROBABLE, COMPLETE, ELEMENTARY AND COMPOUND, INDEPENDENT AND DEPENDENT, COMPLEMENTARY). POPULATION, SAMPLE, UNIT OF ANALYSIS, VARIABLE AND DATA, PARAMETER AND ESTIMATOR. EVALUATION CRITERIA OF ASSOCIATION AND CASUALITY IN MEDICINE. SYSTEMATIC ERROR AND RANDOM ERROR. EPISTEMICAL UNCERTAINTIES AND THEIR TYPES, SOURCES OF EPISTEMICAL UNCERTAINTY. RANDOM UNCERTAINTIES AND THEIR NATURE. MANAGING UNCERTAINTIES IN MEDICAL RESEARCH. RELATIONS BETWEEN VARIABLES. KNOWN AND UNKNOWN RELATIONSHIP. METHOD OF LEAST SQUARES, LINEAR RELATIONSHIP AND CALCULATION OF THE PARAMETERS, CONFIDENCE INTERVAL ON THE ANGLE COEFFICIENT AND ON THE PREDICTION. CORRELATION COEFFICIENT AND HYPOTHESIS TEST ON LINEAR CORRELATION COEFFICIENT. LINEAR REGRESSION AND ANOVA. EXPONENTIAL FUNCTIONS. LINEARIZATION. LINEAR FUNCTION IN THE PARAMETERS AND NON LINEAR FUNCTIONS. LOCAL AND GLOBAL MINIMA. MODEL OF ACTION OF A DRUG. INTRODUCTION TO MULTIVARIATE STATISTICS. DATA MATRIX. STATISTICAL INDEXES. CORRELATION ANALYSIS AND CORRELATION MATRIX. DISTANCE MEASUREMENT. MULTIPLE REGRESSION. RESIDUE ANALYSIS. EXPERIMENTAL DESIGN AND SAMPLING. PLANNING A RESEARCH. VALIDITY. EXPERIMENTAL DESIGN AND SAMPLING. CONFOUNDING VARIABLES AND ARTIFACTS. REDUCE DISTORTION. PROBABILISTIC AND NON-PROBABILISTIC SAMPLING METHODS. CHOICE OF SAMPLING AND SAMPLE SIZE. PLANNING ACCURACY. POWER OF A HYPOTHESIS TEST, INTERPRETATION AND SAMPLE PLANNING OF A TEST. STATISTICAL SIGNIFICANCE AND CLINICAL RELEVANCE. STATISTICS AND EPIDEMIOLOGY. INTRODUCTION TO EPIDEMIOLOGY AND BRANCHES OF EPIDEMIOLOGY. TYPES OF STUDY (OBSERVATIONAL, EXPERIMENTAL, THEORETICAL). DATA SOURCES. NATURAL HISTORY AND DETERMINANTS OF A DISEASE. CAUSALITY CRITERIA. ABSOLUTE AND RELATIVE MEASURES IN EPIDEMIOLOGY (FREQUENCY, RATES, RATIOS, PROPORTIONS). ABSOLUTE AND RELATIVE FREQUENCY. RATES (CRUDE, SPECIFIC, PROPORTIONAL, STANDARDIZED RATE). REPORTS, MORBOSITY RATES. PREVALENCE AND INCIDENCE AND THEIR RELATIONSHIP. ATTACK RATE. MORTALITY AND LETHALITY. RISK ASSESSMENT. EXPERIMENTAL STUDIES. META-ANALYSIS. INFORMATICS (ING-INF/06) 2 CFU UNIT 1 – COMPUTER SCIENCE (DEFINITIONS AND THEORETICAL REMARKS): BASIC CONCEPTS OF COMPUTER SCIENCE AND INFORMATION PROCESSING SYSTEMS; DATA AND INFORMATION; ALGORITHMS AND EXECUTORS; VON NEUMANN ARCHITECTURE; PROGRAMMING LANGUAGES AND TRANSLATORS; PROGRAMS AND PROCESSES; INFORMATION REPRESENTATION; DATA AND INSTRUCTION ENCODING IN DIGITAL COMPUTERS (BINARY INFORMATION REPRESENTATIONS); DIGITAL SIGNALS AND IMAGES. UNIT 2 – HARDWARE & SOFTWARE: TYPES OF CALCULATORS; COMPUTER ARCHITECTURE AND ORGANIZATION; OPERATING SYSTEMS AND BASIC SOFTWARE. UNIT 3 – APPLICATIONS FOR DATA ANALYSIS: BIOSTATISTICS REMARKS; BASIC CONCEPTS OF MACHINE LEARNING TECHNIQUES; USE OF THE ELECTRONIC SPREADSHEETS FOR DESCRIPTIVE AND INFERENTIAL STATISTICS; INTRODUCTION TO THE MICROSOFT EXCEL TOOL (CELLS, REFERENCES, BASIC AND ADVANCED FUNCTIONS); GRAPHICAL REPRESENTATIONS IN MICROSOFT EXCEL (BAR AND PIE DIAGRAMS, DISPERSION PLOTS, BOX PLOTS, BUBBLE PLOTS); DATA GROUPING AND PIVOT TABLES; EXERCISES ON PRACTICAL APPLICATIONS IN BIOMEDICINE. UNIT 4 – COMPUTER NETWORKS: COMMUNICATION ARCHITECTURES AND PROTOCOLS; INTERNET; ISO/OSI MODEL AND TCP/IP PROTOCOL STACK; WORLD WIDE WEB; EMAIL; SEARCH ENGINES; BIBLIOGRAPHIC RESEARCH IN BIOMEDICINE (PUBMED); IT SECURITY. UNIT 5 – HEALTH INFORMATION SYSTEMS: DATABASE MANAGEMENT AND QUERY; COMPANY AND HEALTHCARE INFORMATION SYSTEMS. UNIT 6 (EXTENSION) – APPLICATIONS IN DIGITAL HEALTHCARE: DIGITAL MEDICINE AND BIOMEDICAL INFORMATICS; SENSORS AND WEARABLE DEVICES FOR E-HEALTH; CONCEPTS OF BIONFORMATIC AND MEDICAL IMAGING; INFORMATION WORKFLOWS AND STANDARDS IN HEALTHCARE INFORMATION SYSTEMS; THE DICOM STANDARD FOR MEDICAL IMAGES. FOR DENTISTRY STUDENTS COMMUNITY AND PREVENTIVE DENTISTRY. OBJECTIVE AND METHODS OF PUBLIC DENTAL HEALTH, BASELINE AND GOALS OF COMMUNITY AND PREVENTIVE DENTISTRY, PREVENTIVE DENTISTRY, LEVELS OF PREVENTION, HEALTH EDUCATION EPIDEMIOLOGY. MEASUREMENTS OF FREQUENCY OF HEALTHCARE OCCURRENCE (PREVALENCE AND INCIDENCE), ASSOCIATIVE MEASUREMENTS (RELATIVE RISK AND ODDS RATIO), EPIDEMIOLOGICAL STUDIES (DESCRIPTIVE, CROSS-SECTIONAL, CASE CONTROL, COHORT STUDY, EXPERIMENTAL STUDY), EPIDEMIOLOGICAL STUDIES (SAMPLING AND QUESTIONNAIRE), THE EPIDEMIOLOGICAL STUDY FOR GENE RELATED DISEASES IN THE ORO-MAXILLO-FACIAL REGION, THE EPIDEMIOLOGICAL STUDY FOR RARE DISEASES IN THE ORO-MAXILLO-FACIAL REGION INTRODUCTION TO: ORAL EMBRYOLOGY AND ANATOMY, TOOTH EMBRYOLOGY AND HISTOLOGY, DENTO-PERIODONTAL ANATOMY, ORAL ANATOMY AND PHYSIOLOGY, PRIMARY AND PERMANENT DENTITION ANATOMY. TOOTH ERUPTION. INTRODUCTION TO DENTAL CARIES : EPIDEMIOLOGY AND PREVENTION, INTRODUCTION TO PERIODONTAL DISEASE: EPIDEMIOLOGY AND PREVENTION MODULE: SOCIOLOGY. BASIC RESEARCH AND APPLIED RESEARCH IN HEALTH CARE SETTINGS, RESEARCH DESIGN, HYPOTHESIS FORMULATION RESEARCH, TOOLS QUALITATIVE AND QUANTITATIVE VERIFICATION EXPLANATION, INTERPRETATION, PROCESSING AND MANAGEMENT OF DATA

Contents

MEDICAL STATISTICS MODULE
COMMON/SHARED SYLLABUS
INTRODUCTION TO THE STUDY OF STATISTICS AND PROBABILITY IN THEIR HISTORICAL CONTEXT. USEFULNESS OF KNOWLEDGE ON STATISTICS AND PROBABILITY IN THE CLINICAL FIELD.

PROBABILITY. BASIC DEFINITIONS (EVENT E; NEGATIVE OR COMPLEMENT; UNION, INTERSECTION AND IMPLICATION; INCOMPATIBLE EVENTS AND NECESSARY EVENTS; PARTITION). PROBABILITY ACCORDING TO PASCAL, PROBABILITY OF THE CONTRARY EVENT. TOTAL PROBABILITY THEOREM (INCOMPATIBLE EVENTS, COMPATIBLE EVENTS AND COMPLEMENTARY EVENTS). COMPOUND PROBABILITY. EMPLOYEE AND INDEPENDENT EVENTS. TREE DIAGRAMS AND PROBABILITY CALCULATION ON TREE DIAGRAMS. INDEPENDENCE (DE MOIVRE) AND THEOREMS OF COMPOUND PROBABILITY (FOR INDEPENDENT EVENTS AND DEPENDENT EVENTS). PROBABILITY ACCORDING TO THE FREQUENTIST DEFINITION. EMPIRICAL LAW OF CASE. PROBABILITY ACCORDING TO KOLMOGOROV'S AXIOMATIC APPROACH, AXIOM OF PROBABILITY. BAYES THEOREM. COMBINATORY CALCULATION: ARRANGEMENTS, PERMUTATIONS AND COMBINATIONS.

MEASUREMENT THEORY. RANDOM AND DETERMINISTIC VARIABLE. MEASUREMENT AND TRUE VALUE. MEASURING SCALES. FEATURES OF MEASURING INSTRUMENTS: PRECISION; ACCURACY; SENSITIVITY; RESOLUTION. PROBABILITY DISTRIBUTIONS: CUMULATIVE DISTRIBUTION FUNCTION; DISCRETE DENSITY FUNCTION AND ITS PROPERTIES; PROBABILITY DENSITY FUNCTION AND ITS PROPERTIES; MEAN, VARIANCE AND STANDARD DEVIATION; CHEBYSHEV INEQUALITY; DISCRETE, BINOMIAL, HYPERGEOMETRIC, POISSON, CONTINUOUS, GAUSSIAN DISTRIBUTION; PROPERTY OF THE GAUSSIAN DISTRIBUTION; STANDARDIZED NORMAL DISTRIBUTION; USE OF TABLES AND MATHEMATICAL PROPERTIES USEFUL FOR THEIR USE; DISTRIBUTION OF CHI SQUARE, F OF FISHER, T OF STUDENT. ERRORS: ABSOLUTE AND RELATIVE; AVERAGE VALUE AND TRUE VALUE; ACCURACY AND PRECISION; DEVIATIONS AND REJECTS; AVERAGE DEVIATION AND PERCENTAGE AVERAGE DEVIATION; LIMITS OF RELIANCE; VARIANCE AND STANDARD DEVIATION; SHAPE INDEXES (ASYMMETRY AND CURTOSIS).

CENTRAL LIMIT THEOREM. CONFIDENCE INTERVALS. TYPES OF ASSOCIATION (CAUSAL; SPURIOUS; NON-CAUSAL). CAUSALITY CRITERIA (CONSISTENCE, STRENGTH, SPECIFICITY, TEMPORALITY, CONSISTENCY). SENSITIVITY, SPECIFICITY, FALSE POSITIVES AND FALSE NEGATIVES. POSITIVE AND NEGATIVE PREDICTIVITY. ESTIMATION OF SENSITIVITY AND SPECIFICITY.

INTRODUCTION TO INFERENTIAL STATISTICS. CONFIDENCE INTERVALS: KNOWN SAMPLE AVERAGE (LARGE SAMPLES, UNKNOWN VARIANCE AND SMALL SAMPLE; DIFFERENCE BETWEEN AVERAGES, KNOWING THE POPULATION VARIANCIES AND LARGE SAMPLE OR IN THE CASE OF UNKNOWN VARIANCES); CONFIDENCE INTERVALS FOR POPULATION VARIANCE; ESTIMATION OF THE AVERAGE OF NON-NORMAL POPULATION (CASE OF BERNOULLIAN POPULATION); CONFIDENCE INTERVAL IN THE EVENT OF A DIFFERENCE BETWEEN PROPORTIONS. HYPOTHESIS TEST: HYPOTHESIS TEST; TYPES OF ERROR (FIRST AND SECOND TYPE); LEVEL OF SIGNIFICANCE AND POWER OF A TEST; P-VALUE; HYPOTHESIS TEST BASED ON TESTING THE MEAN (LARGE N OR GAUSSIAN DISTRIBUTION OF PARAMETER WITH KNOWN VARIANCE; SMALL N AND UNKNOWN VARIANCE; TYPICAL VALUES OF THE CRITICAL VALUE); CHECK ON THE SHAPE OF THE DISTRIBUTION (CHI-FRAME TEST OF ADAPTATION); PROPORTION HYPOTHESIS TEST; HYPOTHESIS TEST ON THE DIFFERENCE BETWEEN TWO MEANS (NORMAL DISTRIBUTIONS WITH KNOWN VARIANCES; UNKNOWN VARIANCES); T TEST FOR PAIRED DATA; TEST OF EQUALITY BETWEEN PROPORTIONS; CHI SQUARE TEST OF INDEPENDENCE AND CONTINGENCY TABLES; DEGREE OF ASSOCIATION; ODDS RATIO AND RISK RATIO AND CONFIDENCE INTERVALS FOR ODDS RATIO AND RISK RATIO (SIGNIFICANCE OF ASSOCIATION). ANALYSIS OF VARIANCE (ANOVA): GENERAL PRINCIPLES; ANOVA MODEL AND ASSUMPTIONS ON THE MODEL; HOMOSKEDASTICITY OF RANDOM VARIABLES; DEVIANCE (VERIFICATION BETWEEN GROUPS) AND RESIDUAL VARIATION WITHIN GROUPS; BASIC AND ALTERNATIVE PROCEDURE FOR ANOVA; POST HOC COMPARISONS; ORTHOGONAL COMPARISONS AND PROCEDURE; REPEAT TESTS; DATA MATRIX; MULTI-FACTOR MODEL; CALCULATION WITH EXPERIMENT 2 × 2; DEVIANCE BROKEN BETWEEN GROUPS; COMPARISONS; INTERACTING AND NON-INTERACTING FACTORS (GRAPHIC REPRESENTATION).

ONLY FOR MEDICINE STUDENTS
IN ADDITION TO THE ABOVE TOPICS:

INTRODUCTION TO EVIDENCE BASED MEDICINE: THE EBM CYCLE, THE CLINICAL DEMAND (TYPES OF CLINICAL QUESTION; FINER CRITERION; PICO CRITERION); HIERARCHY OF EVIDENCE. TYPES OF STUDY (CASE SERIES, SECTIONAL STUDIES, COHORT, CASE-CONTROL, CLINICAL TRIAL). INCLUSION AND EXCLUSION CRITERIA.
BASIC DEFINITIONS: TYPES OF EVENT (EQUIPROBABLE, COMPLETE, ELEMENTARY AND COMPOUND, INDEPENDENT AND DEPENDENT, COMPLEMENTARY). POPULATION, SAMPLE, UNIT OF ANALYSIS, VARIABLE AND DATA, PARAMETER AND ESTIMATOR. EVALUATION CRITERIA OF ASSOCIATION AND CASUALITY IN MEDICINE.
SYSTEMATIC ERROR AND RANDOM ERROR. EPISTEMICAL UNCERTAINTIES AND THEIR TYPES, SOURCES OF EPISTEMICAL UNCERTAINTY. RANDOM UNCERTAINTIES AND THEIR NATURE. MANAGING UNCERTAINTIES IN MEDICAL RESEARCH.

RELATIONS BETWEEN VARIABLES. KNOWN AND UNKNOWN RELATIONSHIP. METHOD OF LEAST SQUARES, LINEAR RELATIONSHIP AND CALCULATION OF THE PARAMETERS, CONFIDENCE INTERVAL ON THE ANGLE COEFFICIENT AND ON THE PREDICTION. CORRELATION COEFFICIENT AND HYPOTHESIS TEST ON LINEAR CORRELATION COEFFICIENT. LINEAR REGRESSION AND ANOVA. EXPONENTIAL FUNCTIONS. LINEARIZATION. LINEAR FUNCTION IN THE PARAMETERS AND NON LINEAR FUNCTIONS. LOCAL AND GLOBAL MINIMA. MODEL OF ACTION OF A DRUG. INTRODUCTION TO MULTIVARIATE STATISTICS. DATA MATRIX. STATISTICAL INDEXES. CORRELATION ANALYSIS AND CORRELATION MATRIX. DISTANCE MEASUREMENT. MULTIPLE REGRESSION. RESIDUE ANALYSIS.

EXPERIMENTAL DESIGN AND SAMPLING. PLANNING A RESEARCH. VALIDITY. EXPERIMENTAL DESIGN AND SAMPLING. CONFOUNDING VARIABLES AND ARTIFACTS. REDUCE DISTORTION. PROBABILISTIC AND NON-PROBABILISTIC SAMPLING METHODS. CHOICE OF SAMPLING AND SAMPLE SIZE. PLANNING ACCURACY. POWER OF A HYPOTHESIS TEST, INTERPRETATION AND SAMPLE PLANNING OF A TEST. STATISTICAL SIGNIFICANCE AND CLINICAL RELEVANCE.

STATISTICS AND EPIDEMIOLOGY. INTRODUCTION TO EPIDEMIOLOGY AND BRANCHES OF EPIDEMIOLOGY. TYPES OF STUDY (OBSERVATIONAL, EXPERIMENTAL, THEORETICAL). DATA SOURCES. NATURAL HISTORY AND DETERMINANTS OF A DISEASE. CAUSALITY CRITERIA. ABSOLUTE AND RELATIVE MEASURES IN EPIDEMIOLOGY (FREQUENCY, RATES, RATIOS, PROPORTIONS). ABSOLUTE AND RELATIVE FREQUENCY. RATES (CRUDE, SPECIFIC, PROPORTIONAL, STANDARDIZED RATE). REPORTS, MORBOSITY RATES. PREVALENCE AND INCIDENCE AND THEIR RELATIONSHIP. ATTACK RATE. MORTALITY AND LETHALITY. RISK ASSESSMENT. EXPERIMENTAL STUDIES. META-ANALYSIS.

INFORMATICS (ING-INF/06) 2 CFU

UNIT 1 – COMPUTER SCIENCE (DEFINITIONS AND THEORETICAL REMARKS): BASIC CONCEPTS OF COMPUTER SCIENCE AND INFORMATION PROCESSING SYSTEMS; DATA AND INFORMATION; ALGORITHMS AND EXECUTORS; VON NEUMANN ARCHITECTURE; PROGRAMMING LANGUAGES AND TRANSLATORS; PROGRAMS AND PROCESSES; INFORMATION REPRESENTATION; DATA AND INSTRUCTION ENCODING IN DIGITAL COMPUTERS (BINARY INFORMATION REPRESENTATIONS); DIGITAL SIGNALS AND IMAGES.

UNIT 2 – HARDWARE & SOFTWARE: TYPES OF CALCULATORS; COMPUTER ARCHITECTURE AND ORGANIZATION; OPERATING SYSTEMS AND BASIC SOFTWARE.

UNIT 3 – APPLICATIONS FOR DATA ANALYSIS: BIOSTATISTICS REMARKS; BASIC CONCEPTS OF MACHINE LEARNING TECHNIQUES; USE OF THE ELECTRONIC SPREADSHEETS FOR DESCRIPTIVE AND INFERENTIAL STATISTICS; INTRODUCTION TO THE MICROSOFT EXCEL TOOL (CELLS, REFERENCES, BASIC AND ADVANCED FUNCTIONS); GRAPHICAL REPRESENTATIONS IN MICROSOFT EXCEL (BAR AND PIE DIAGRAMS, DISPERSION PLOTS, BOX PLOTS, BUBBLE PLOTS); DATA GROUPING AND PIVOT TABLES; EXERCISES ON PRACTICAL APPLICATIONS IN BIOMEDICINE.

UNIT 4 – COMPUTER NETWORKS: COMMUNICATION ARCHITECTURES AND PROTOCOLS; INTERNET; ISO/OSI MODEL AND TCP/IP PROTOCOL STACK; WORLD WIDE WEB; EMAIL; SEARCH ENGINES; BIBLIOGRAPHIC RESEARCH IN BIOMEDICINE (PUBMED); IT SECURITY.

UNIT 5 – HEALTH INFORMATION SYSTEMS: DATABASE MANAGEMENT AND QUERY; COMPANY AND HEALTHCARE INFORMATION SYSTEMS.

UNIT 6 (EXTENSION) – APPLICATIONS IN DIGITAL HEALTHCARE: DIGITAL MEDICINE AND BIOMEDICAL INFORMATICS; SENSORS AND WEARABLE DEVICES FOR E-HEALTH; CONCEPTS OF BIONFORMATIC AND MEDICAL IMAGING; INFORMATION WORKFLOWS AND STANDARDS IN HEALTHCARE INFORMATION SYSTEMS; THE DICOM STANDARD FOR MEDICAL IMAGES.

FOR DENTISTRY STUDENTS

COMMUNITY AND PREVENTIVE DENTISTRY. OBJECTIVE AND METHODS OF PUBLIC DENTAL HEALTH, BASELINE AND GOALS OF COMMUNITY AND PREVENTIVE DENTISTRY, PREVENTIVE DENTISTRY, LEVELS OF PREVENTION, HEALTH EDUCATION
EPIDEMIOLOGY. MEASUREMENTS OF FREQUENCY OF HEALTHCARE OCCURRENCE (PREVALENCE AND INCIDENCE), ASSOCIATIVE MEASUREMENTS (RELATIVE RISK AND ODDS RATIO), EPIDEMIOLOGICAL STUDIES (DESCRIPTIVE, CROSS-SECTIONAL, CASE CONTROL, COHORT STUDY, EXPERIMENTAL STUDY), EPIDEMIOLOGICAL STUDIES (SAMPLING AND QUESTIONNAIRE), THE EPIDEMIOLOGICAL STUDY FOR GENE RELATED DISEASES IN THE ORO-MAXILLO-FACIAL REGION, THE EPIDEMIOLOGICAL STUDY FOR RARE DISEASES IN THE ORO-MAXILLO-FACIAL REGION
INTRODUCTION TO: ORAL EMBRYOLOGY AND ANATOMY, TOOTH EMBRYOLOGY AND HISTOLOGY, DENTO-PERIODONTAL ANATOMY, ORAL ANATOMY AND PHYSIOLOGY, PRIMARY AND PERMANENT DENTITION ANATOMY. TOOTH ERUPTION. INTRODUCTION TO DENTAL CARIES : EPIDEMIOLOGY AND PREVENTION, INTRODUCTION TO PERIODONTAL DISEASE: EPIDEMIOLOGY AND PREVENTION

MODULE: SOCIOLOGY. BASIC RESEARCH AND APPLIED RESEARCH IN HEALTH CARE SETTINGS, RESEARCH DESIGN, HYPOTHESIS FORMULATION RESEARCH, TOOLS QUALITATIVE AND QUANTITATIVE VERIFICATION EXPLANATION, INTERPRETATION, PROCESSING AND MANAGEMENT OF DATA

	Teaching Methods
	LECTURES AND EXERCISES

	Verification of learning
	WRITTEN AND/OR ORAL EXAMINATION. FOR MEDICINE STUDENTS, THE EXAMINATION WILL BE DIVIDED INTO THREE PARTS. THE FIRST TWO PARTS, AIMED AT EVALUATING THE LEARNING RELATIVE TO THE COMPUTER INFORMATION MODULE, WILL BE CONSISTED OF A DESIGN TASK, TO BE CARRIED OUT ON THE BASIS OF SEPARATE TRACKS ADMINISTERED BY THE TEACHER, AND OF A WRITTEN TEST, WHICH WILL COVER ALL THE DIDACTIC UNITS OF THE MODULE OF COMPUTER SCIENCE. THE ORAL EXAMINATION WILL BE AIMED AT CONFIRMING THE LEVEL OF KNOWLEDGE AND ASCERTAINING THE UNDERSTANDING ABILITY ACHIEVED BY THE STUDENT ON THE THEORETICAL AND METHODOLOGICAL CONTENTS INDICATED IN THE COURSE PROGRAMME, INCLUDING BOTH TOPICS OF MEDICAL STATISTICS AND COMPUTER SCIENCE. FOR DENTISTRY STUDENTS THE EXAMINATION WILL BE WRITTEN AND/OR ORAL. THE EXAMINATION WILL BE AIMED AT CONFIRMING THE LEVEL OF KNOWLEDGE AND ASCERTAINING THE UNDERSTANDING ABILITY ACHIEVED BY THE STUDENT ON THE THEORETICAL AND METHODOLOGICAL CONTENTS INDICATED IN THE COURSE PROGRAMME FOR ALL STUDENTS, THE EXAMINATION TEST IS AIMED AT ASSESSING THE STUDENT'S ABILITY TO HAVE LEARNED THE FUNDAMENTAL PRINCIPLES OF THE VARIOUS DISCIPLINES AS WELL AS THE STUDENT'S COMMUNICATION CAPACITY WITH PROPERTIES OF LANGUAGE AND AUTONOMOUS ORGANIZATION OF THE EXPOSURE ON THE SAME TOPICS WITH THEORETICAL CONTENT . THE MARK, EXPRESSED OUT OF THIRTY WITH POSSIBLE LAUD, WILL DEPEND ON THE MATURITY ACQUIRED ON THE CONTENTS OF THE INTEGRATED COURSE. FOR THE PURPOSE OF THE ALLOCATION OF HONOR, THE QUALITY OF THE PRESENTATION WILL BE TAKEN INTO ACCOUNT, IN TERMS OF THE USE OF APPROPRIATE SCIENTIFIC LANGUAGE AND THE DEMONSTRATED INDEPENDENCE OF JUDGMENTS.

Verification of learning

WRITTEN AND/OR ORAL EXAMINATION.

FOR MEDICINE STUDENTS, THE EXAMINATION WILL BE DIVIDED INTO THREE PARTS. THE FIRST TWO PARTS, AIMED AT EVALUATING THE LEARNING RELATIVE TO THE COMPUTER INFORMATION MODULE, WILL BE CONSISTED OF A DESIGN TASK, TO BE CARRIED OUT ON THE BASIS OF SEPARATE TRACKS ADMINISTERED BY THE TEACHER, AND OF A WRITTEN TEST, WHICH WILL COVER ALL THE DIDACTIC UNITS OF THE MODULE OF COMPUTER SCIENCE.
THE ORAL EXAMINATION WILL BE AIMED AT CONFIRMING THE LEVEL OF KNOWLEDGE AND ASCERTAINING THE UNDERSTANDING ABILITY ACHIEVED BY THE STUDENT ON THE THEORETICAL AND METHODOLOGICAL CONTENTS INDICATED IN THE COURSE PROGRAMME, INCLUDING BOTH TOPICS OF MEDICAL STATISTICS AND COMPUTER SCIENCE.

FOR DENTISTRY STUDENTS THE EXAMINATION WILL BE WRITTEN AND/OR ORAL. THE EXAMINATION WILL BE AIMED AT CONFIRMING THE LEVEL OF KNOWLEDGE AND ASCERTAINING THE UNDERSTANDING ABILITY ACHIEVED BY THE STUDENT ON THE THEORETICAL AND METHODOLOGICAL CONTENTS INDICATED IN THE COURSE PROGRAMME

FOR ALL STUDENTS, THE EXAMINATION TEST IS AIMED AT ASSESSING THE STUDENT'S ABILITY TO HAVE LEARNED THE FUNDAMENTAL PRINCIPLES OF THE VARIOUS DISCIPLINES AS WELL AS THE STUDENT'S COMMUNICATION CAPACITY WITH PROPERTIES OF LANGUAGE AND AUTONOMOUS ORGANIZATION OF THE EXPOSURE ON THE SAME TOPICS WITH THEORETICAL CONTENT . THE MARK, EXPRESSED OUT OF THIRTY WITH POSSIBLE LAUD, WILL DEPEND ON THE MATURITY ACQUIRED ON THE CONTENTS OF THE INTEGRATED COURSE. FOR THE PURPOSE OF THE ALLOCATION OF HONOR, THE QUALITY OF THE PRESENTATION WILL BE TAKEN INTO ACCOUNT, IN TERMS OF THE USE OF APPROPRIATE SCIENTIFIC LANGUAGE AND THE DEMONSTRATED INDEPENDENCE OF JUDGMENTS.

	Texts
	TEACHING UNIT: MEDICAL STATISTICS BASIC REFERENCE BOOK M WHITLOCK, D SCHLUTER. ANALISI STATISTICA DEI DATI BIOLOGICI. ZANICHELLI. ISBN 978-88-08-42014-5 OTHER BOOKS WAYNE W. DANIEL, CHAD L. CROSS, BIOSTATISTICA, EDISES JOHN R. TAYLOR, INTRODUZIONE ALL’ANALISI DEGLI ERRORI, ED. ZANICHELLI. PHILIP BEVINGTON, D. KEITH ROBINSON, DATA REDUCTION AND ERROR ANALYSIS FOR THE PHYSICAL SCIENCES, ED. MCGRAW-HILL. MODULE OF COMMUNITY AND PREVENTIVE DENTISTRY: "ODONTOIATRIA DI COMUNITA". STROHMENGER L., FERRO R. MASSON 2003 AN EVENTUAL FURTHER LIST OF READINGS WILL BE INDICATED AT THE BEGINNING OF THE COURSE MODULE:SOCIOLOGY THE LIST OF READINGS WILL BE DISTRIBUTED AT THE BEGINNING OF THE COURSE. STUDY MATERIAL WILL BE PUT AT STUDENTS DISPOSAL FROM THE TEACHER DURING THE LESSONS.

Texts

TEACHING UNIT: MEDICAL STATISTICS
BASIC REFERENCE BOOK
M WHITLOCK, D SCHLUTER. ANALISI STATISTICA DEI DATI BIOLOGICI. ZANICHELLI. ISBN 978-88-08-42014-5

OTHER BOOKS
WAYNE W. DANIEL, CHAD L. CROSS, BIOSTATISTICA, EDISES
JOHN R. TAYLOR, INTRODUZIONE ALL’ANALISI DEGLI ERRORI, ED. ZANICHELLI.

PHILIP BEVINGTON, D. KEITH ROBINSON, DATA REDUCTION AND ERROR ANALYSIS FOR THE PHYSICAL SCIENCES, ED. MCGRAW-HILL.

MODULE OF COMMUNITY AND PREVENTIVE DENTISTRY:
"ODONTOIATRIA DI COMUNITA". STROHMENGER L., FERRO R. MASSON 2003 AN EVENTUAL FURTHER LIST OF READINGS WILL BE INDICATED AT THE BEGINNING OF THE COURSE

MODULE:SOCIOLOGY
THE LIST OF READINGS WILL BE DISTRIBUTED AT THE BEGINNING OF THE COURSE. STUDY MATERIAL WILL BE PUT AT STUDENTS DISPOSAL FROM THE TEACHER DURING THE LESSONS.

	More Information
	FOR MORE DETAILED INFORMATION ON THE PROGRAM, NOTICES AND COMMUNICATIONS ABOUT THE UNIT OF MEDICAL STATISTICS, PLEASE REFER TO THE WEBSITES OF THE PROFESSORS:

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