Trisha M. Greenhalgh (UK University of Oxford), Paul Dijkstra (UK University of Oxford)

How to Read a Paper

the Basics of Evidence-Based Healthcare

• Broschiertes Buch

Produktbeschreibung

Learn to assess published research in this best-selling introduction to evidence-based healthcare Evidence-based practices have revolutionized medical care. Clinical and scientific papers have something to offer practitioners at every level of the profession, from students to established clinicians in medicine, nursing and allied professions. Novices are often intimidated by the idea of reading and appraising the research literature. How to Read a Paper demystifies this process with a thorough, engaging introduction to how clinical research papers are constructed and how to evaluate them. Now fully updated to incorporate new areas of research, readers of the seventh edition of How to Read a Paper will also find: * A careful balance between the principles of evidence-based healthcare and clinical practice * New chapters covering consensus methods, mechanistic evidence, big data and artificial intelligence * Detailed coverage of subjects like assessing methodological quality, systemic reviews and meta-analyses, qualitative research, and more. How to Read a Paper is ideal for all healthcare students and professionals seeking an accessible introduction to evidence-based healthcare - particularly those sitting undergraduate and postgraduate exams and preparing for interviews.

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Produktdetails

• How To
• Verlag: John Wiley & Sons Inc
• 7 ed
• Seitenzahl: 352
• Erscheinungstermin: 26. Dezember 2024
• Englisch
• Abmessung: 213mm x 140mm x 24mm
• Gewicht: 488g
• ISBN-13: 9781394206902
• ISBN-10: 1394206909
• Artikelnr.: 71184749

Autorenporträt

Trisha Greenhalgh is a general practitioner and Professor of Primary Care Health Sciences and Fellow of Green Templeton College at the University of Oxford. Paul Dijkstra is a sport and exercise medicine physician and Director of Medical Education at Aspetar Orthopaedic and Sports Medicine Hospital in Doha, Qatar. He has an academic affiliation with the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences at the University of Oxford.

Inhaltsangabe

Foreword to the first edition by Professor Sir David Weatherall xii
Preface to the seventh edition xiv
Preface to the first edition xvii
Acknowledgements xix
Chapter 1 Why read papers at all? 1
Does 'evidence- based medicine' simply mean 'reading papers in medical
journals'? 1
Why do people sometimes groan when you mention evidence- based healthcare?
4
Before you start: formulate the problem 11
Exercises based on this chapter 13
References 14
Chapter 2 Searching the literature 15
The information jungle 15
What are you looking for? 16
Levels upon levels of evidence 17
Synthesised sources: systems, summaries and syntheses 18
Pre-appraised sources: synopses of systematic reviews and primary studies
21
Specialised resources 22
Primary studies: tackling the jungle 23
One-stop shopping: federated search engines 25
Using artificial intelligence to search the literature 25
Asking for help and asking around 26
Online tutorials for effective searching 26
Exercises based on this chapter 27
References 28
Chapter 3 Getting your bearings: what is this paper about? 30
The science of 'trashing' papers 30
Three preliminary questions to get your bearings 32
What are randomised controlled trials and why do they matter? 34
What are cohort studies? 38
What are case-control studies? 40
What are cross-sectional surveys? 40
What are case reports? 41
The traditional hierarchy of evidence 42
Exercises based on this chapter 43
References 43
Chapter 4 Assessing methodological quality 45
Was the study original? 45
Who is the study about? 46
Was the design of the study sensible? 47
Was bias avoided or minimised? 49
Was assessment 'blind'? 54
Were preliminary statistical questions addressed? 55
A note on ethical considerations 58
Summing up 59
Exercises based on this chapter 60
References 60
Chapter 5 Statistics for the non-statistician 63
How can non-statisticians evaluate statistical tests? 63
Have the authors set the scene correctly? 65
Paired data, tails and outliers 71
Correlation, regression and causation 72
Probability and confidence 74
The bottom line (quantifying the chance of benefit and harm) 77
Summary 79
Exercises based on this chapter 79
References 80
Chapter 6 Papers that report clinical trials of simple interventions 82
What is a clinical trial? 82
Drug trials: 'evidence' and marketing 83
Making decisions about therapy 86
Surrogate endpoints 87
What information to expect in a paper describing a randomised controlled
trial: the CONSORT statement 91
Getting worthwhile evidence from pharmaceutical representatives 91
A note on vaccine trials 94
Exercises based on this chapter 95
References 95
Chapter 7 Papers that report trials of complex interventions 99
Complex interventions 99
Ten questions to ask about a paper describing a complex intervention 101
Exercises based on this chapter 106
References 107
Chapter 8 Papers that report diagnostic or screening tests 109
Ten suspects in the dock 109
Validating diagnostic tests against a gold standard 110
Ten questions to ask about a paper that claims to validate a diagnostic or
screening test 115
Likelihood ratios 119
Clinical prediction models 122
Exercises based on this chapter 124
References 125
Chapter 9 Papers that summarise other papers (systematic reviews and
meta-analyses) 128
When is a review systematic? 128
Evaluating systematic reviews: five questions to ask 131
Meta-analysis for the non-statistician 137
Explaining heterogeneity 142
New approaches to systematic review 145
Exercises based on this chapter 146
References 146
Chapter 10 Papers that advise you what to do (guidelines) 151
The great guidelines debate 151
Ten questions to ask about a clinical guideline 155
Exercises based on this chapter 162
References 162
Chapter 11 Papers that estimate what things cost (health economic
evaluations) 164
What is an economic evaluation? 164
Health economics studies: two key approaches 166
Costs and benefits of health interventions 167
Measuring the value of health states 168
Quality-adjusted life-years 169
Low-value health: choosing wisely 171
Twelve questions to ask about a health economic evaluation 172
Conclusion 176
Exercises based on this chapter 176
References 177
Chapter 12 Papers that go beyond numbers (qualitative research) 179
What is qualitative research? 179
Summarising and synthesising qualitative research 183
Nine questions to ask about a qualitative research paper 184
Conclusion 191
Exercises based on this chapter 192
References 192
Chapter 13 Papers that report questionnaire research 195
The rise and rise of questionnaire research 195
Ten questions to ask about a paper describing a questionnaire study 196
Exercises based on this chapter 205
References 206
Chapter 14 Papers that report quality improvement case studies 208
What are quality improvement studies and how should we research them? 208
Ten questions to ask about a paper describing a quality improvement
initiative 210
Conclusion 217
Exercises based on this chapter 217
References 218
Chapter 15 Papers that describe genetic association studies 220
The three eras of human genetic studies (so far) 220
What is a genome-wide association study? 222
Clinical applications of genome-wide association studies 225
Direct- to- consumer genetic testing 226
Mendelian randomisation studies 227
Epigenetics: a space to watch 228
Ten questions to ask about a genetic association study 230
Exercises based on this chapter 234
References 234
Chapter 16 Applying evidence with patients 237
The patient perspective 237
Patient- reported outcome measures 239
Shared decision- making 240
Option grids 243
n-of-1 trials and other individualised approaches 244
Exercises based on this chapter 246
References 247
Contents xi
Chapter 17 Papers on artificial intelligence in healthcare 249
Introduction 249
Artificial intelligence 251
Big data 253
Machine learning 254
Generative artificial intelligence: large language and multimodal models
254
Ethical principles for the use of artificial intelligence for health 255
Appraising artificial intelligence papers: a plethora of checklists 256
Ten questions to ask about a paper that reports AI studies in healthcare
260
Summary 264
Exercises based on this chapter 264
References 265
Chapter 18 EBM+: the importance of mechanistic evidence 268
What is mechanistic evidence? An example 268
The many types of mechanistic evidence and a preliminary hierarchy 269
EBM+ means 'both and', not 'either or' 270
Mechanistic evidence in the COVID-19 pandemic 272
Exercises based on this chapter 275
References 276
Chapter 19 Papers that report consensus exercises 278
Why are consensus method papers important? 279
How do experts choose and reach consensus on a specific topic? 279
Consensus methods 281
Ten questions to ask about a paper that reports a consensus statement 285
Exercises based on this chapter 290
References 291
Chapter 20 Criticisms of evidence-based healthcare 293
What's wrong with evidence-based healthcare when it's done badly? 293
What's wrong with evidence-based healthcare when it's done well? 296
Why is 'evidence-based policymaking' so hard to achieve? 299
Exercises based on this chapter 301
References 301
Appendix 1 Checklists for finding, appraising and implementing evidence 304
Appendix 2 Assessing the effects of an intervention 316
Index 317