Translational Neurosurgery.

Format:

Book

Author/Creator:

Eltorai, Adam E. M.

Series:

Handbook for Designing and Conducting Clinical and Translational Research Series

Language:

English

Subjects (All):

Clinical trials.

Nervous system.

Physical Description:

1 online resource (734 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Translational Neurosurgery provides a comprehensive overview reflecting the depth and breadth of the field of translational research focused on neurosurgery, with input from a distinguished team of basic and clinical investigators.

Contents:

Front Cover

Translational Neurosurgery

Copyright

Contents

Contributors

Preface

Introduction

Keywords:

Objectives

Structure of the Book

Discovery

Development

Evaluation

Implementation

Optimization

How to Use This Book

Clinical Emphasis

Conclusion

Further Reading and Resources:

I Introduction

1 - Translational research and the implementation process

Key points

Introductory section

The false dichotomy: pure vs. applied research

Translational research and implementation strategies

Get started

Potential pitfalls

References

Further reading

2 - Scientific methods: Problems and paradigms

Scientific sensemaking

Relationship between theory and data

Research paradigms and paradigm change

Examples

II Preclinical

3 - What problem are you solving?

Introductory section/why it matters

Examples of …

How to ask the right research questions

Importance and feasibility

Measurability

Types of problems for translational research

New target discovery

Drug discovery and alternative delivery methods of the existing drug

Drug repositioning

Resources

4 - Types of interventions

Examples of medical intervention types

Drug discovery

Medical device development

Diagnostic tests

Behavior change interventions

5. General principles of drug testing

In vitro and in vivo

Harm, toxicity, and dosing

Good Laboratory Practices

Disciplines and common lab methods/techniques

Blotting techniques.

Polymerase chain reaction (PCR)

Microscopic techniques

Cell culture techniques

Spectroscopic techniques

Flow cytometry

Bioinformatics tools

Budgetary considerations

Examples of drug testing in neurosurgery

6 - Neural network-based neurosurgical device discovery and prototypes

Demands for neurosurgical devices in the network neurosurgery period

Neurosurgical robotics for increased precision and intelligence

Advanced spectroscopy technology for invisible surgical boundaries

Noninvasive multimodal monitoring for convenience

Perspective and conclusion

Get Started

7 - Diagnostic method evaluation

The importance of diagnostic testing in clinical and translational research

Challenges faced by diagnostic testing in translational and clinical research

Ask a clear healthcare or clinically relevant question

Determine the gold standard

Select research objects

Estimate sample size

Establish the optimal cutoff value

Evaluate diagnostic indicators

Write a thesis report

Examples of diagnostic testing

Glioma

Acute ischemic stroke

Alzheimer's disease

8 - Artificial intelligence in neurosurgery

Examples of artificial intelligence application in neurosurgery

The intersection of artificial intelligence and neuroscience

How artificial intelligence can help predict neurosurgical outcomes

The role of artificial intelligence in planning neurosurgery

Acknowledgments

Suggested reading

III Statistical principles

9 - Basic statistical principles.

Key points

Why it matters

Accuracy and precision

Prevalence and incidence

Measures of accuracy

True and false, positive and negative

Sensitivity and specificity

Positive and negative predictive values

Quantifying accuracy

Measures of precision

Confidence intervals

Misconceptions

P-values

Definition

Measures of risk

Odds ratio

Risk ratio

Attributable risk

Absolute risk reduction

Number needed to treat

Number needed to harm

Quantifying risk and uncertainty I

Quantifying risk and uncertainty II

Pitfalls

10 - Distributions

Probability

Sampling

Distribution center and spread

Discrete distributions

Binomial

Poisson

Continuous distributions

Normal

11 - Hypothesis testing and error

Theory, constructs, and hypotheses

Hypothesis testing

Null hypothesis significance testing

Conditions for NHST

Replication crises and the future of translational research

12 - Concept of power in statistical testing of hypothesis

Examples of

13 - Continuous variable analyses: t-test, Mann-Whitney U test, and Wilcoxon signed-rank test

Parametric method: t-tests

One-sample t-test for one population mean

Two-sample t-test for two population means

Paired t-test based on paired samples

Nonparametric methods.

Wilcoxon signed-rank test for one population median

Mann-Whitney U test for two independent samples

Get started (section that clearly defines the reader's action items)

Potential pitfalls (3-5 pitfalls to avoid)

Resources (additional reading, consultants, contractors)

14 - Categorical variable analyses: Chi-square, Fisher's exact, and Mantel-Haenszel

Chi-square test

Fisher's exact test

Mantel-Haenszel test

15 - Comparing groups using analysis of variance

How to carry out an ANOVA

Main assumptions of ANOVA

Examples in the literature

16 - Correlation

Examples of ordinal or skewed observations

Interpreting correlation coefficients

Summary

17 - Basic science statistics

Background

Samples

Experimental design

Presenting data

18 - Sample size calculation

Principles

19 - Getting started with statistical software

Key takeaways

Choosing a software

Example with real data: R

A note on R libraries

Example with real data: SAS

Concluding thoughts

Resources for learning

R language

SAS language

IV Clinical: Study types

20 - Outcome measures: The properties and process of measurement

Kinds of variable

Independent variables

Dependent variables

Other variables

The meaning of measurement

Classical measurement theory.

Sources of error and bias

Descriptive and inferential statistics

Types of outcome measures

Outcome measures and sensemaking

21 - Clinical: Study types-Case series: Design, measures, and classic example

Why it matters?

Defined terms

Definition of the study type

Benefits and limitations

Designing a case series

Illustrative examples

Standard research team personnel and roles

Budgetary considerations and how to fund this stage

Additional readings

Videos

22 - Case-control studies

Step-by-step guide for calculating odds ratios and relative risks

Expanded examples using OR and RR calculations

Study design

Interpretation

Three examples in the published literature

23 - Cross-sectional study: Design, measures, classic example

Step-by-step guide to designing a cross-sectional study

Examples of cross-sectional study design

Example 1: vestibular schwannoma diagnosis and management5

Example 2: prevalence of intracranial aneurysms in Marfan syndrome patients6

Example 3: patient satisfaction with telemedicine for spine care: a cross-sectional study7

Research team personnel

24 - Approaching longitudinal research to answer clinical questions over time

Getting started

Benefits and potential pitfalls

Examples of longitudinal research

Quantifying comparative and observational rupture risk for unruptured aneurysms

Determining the risk of aneurysm at a population level.

Developing a radiographic prediction scale for clinical vasospasm.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-323-90523-4

9780323905237

OCLC:

1552585773