Related Experiment Video
Updated: Feb 1, 2026
12:00
A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
36.1K
Systematic reviews of clinical practice guidelines: a methodological guide
Amy Johnston1, Shannon E Kelly1, Shu-Ching Hsieh1
1Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, Ontario K1Y 4W7, Canada.
Journal of Clinical Epidemiology
|December 12, 2018
Summary
This guidance paper offers structured direction for conducting systematic reviews (SRs) of clinical practice guidelines (CPGs). It details methods for rigorous SRs of CPGs, ensuring transparency and quality in clinical epidemiology research.
Area of Science:
- Clinical Epidemiology
- Health Services Research
- Evidence Synthesis
Background:
- Systematic reviews (SRs) of clinical practice guidelines (CPGs) present unique methodological challenges compared to other SRs.
- Tailored approaches are necessary for the design and execution of SRs of CPGs due to their nature as research syntheses and guidance statements.
Purpose of the Study:
- To provide review authors with structured direction for designing and conducting methodologically rigorous SRs of CPGs.
- To outline suggested methodology for all stages of an SR of CPGs, including a case exemplar.
Main Methods:
- Development of a "PICAR" statement to define research questions and eligibility criteria.
- Assessment of CPG quality using validated appraisal tools.
- Transparent extraction, analysis, and summarization of data.
Main Results:
- Consideration of review context and unique characteristics of CPGs is crucial throughout the review process.
- Methodological tailoring is often required to efficiently address objectives and timelines for SRs of CPGs.
Conclusions:
- SRs of CPGs are valuable for identifying, assessing, and summarizing clinical guidance.
- All SRs of CPGs must adhere to rigorous methodological standards and transparent reporting practices.
Related Concept Videos
01:15Systematic Error: Methodological and Sampling Errors
11.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.0K
01:10Review and Preview
8.4K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Percentiles are a type of fractile that partition data into...
8.4K
01:13Review and Preview
11.3K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
11.3K
01:20Random and Systematic Errors
14.9K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.9K
01:11Guidelines for Writing Outcome
3.9K
When developing expected outcomes for a patient care plan, the nurse should adhere to the following recommendations:
Patient outcomes reflect the patient's response to the goal rather than what the nurse aims to achieve. Terminology should be observable and measurable to avoid the reader's interpretation. The desired outcome should be realistic and achievable in the designated care timeframe. Expected outcomes should align with adjunctive therapies. The outcome should enhance care...
Patient outcomes reflect the patient's response to the goal rather than what the nurse aims to achieve. Terminology should be observable and measurable to avoid the reader's interpretation. The desired outcome should be realistic and achievable in the designated care timeframe. Expected outcomes should align with adjunctive therapies. The outcome should enhance care...
3.9K
01:30Guidelines for Nursing Documentation I
1.9K
Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
1.9K