Science IQ

Welcome to the post on qualitative research. Qualitative research is relatively new in the field of health sciences, so, I thought that it would be good to discuss a few characteristics of qualitative researchers. Qualitative research differs from quantitative, and so do the qualities of the researchers. Here are a few qualities which a qualitative researcher must possess to carry out research efficiently.

• Bricoleur – Qualitative researcher collects information from experiences, life stories, observations, interview, historical monuments, etc.  Qualitative researcher is considered a ‘Bricoleur’ which means ‘quilt maker’ who brings together bits and pieces of cloth to create a beautiful quilt or bricolage. Similarly, qualitative researcher joins pieces of information from various sources to create new knowledge, or find meaning in a given phenomenon.

• Researcher as an integral part of context – Qualitative researchers know that their presence influence the context and their gender, race, caste, background, etc. has an impact on the phenomenon being studied. The qualitative researcher is not separated from the context but becomes an active part of this interactive process.

• Multi-tasking – A qualitative researcher does multiple tasks together, such as observing a phenomenon, interviewing or asking questions, self-reflection, analysis, and interpreting the data. Unlike quantitative research, qualitative data analysis occurs inside the mind of the researcher. Making meaning out of the information collected cannot be done by any software but by the researcher himself.

• Research as an art – Qualitative researchers know that research is a complex and refined art wherein the researcher uses different patterns and colors to bring out the true meaning of a phenomenon. Qualitative researchers bridge chunks of information to create a dense collage-like masterpiece.

• Phenomena in a natural context – Qualitative researchers study a phenomenon in its natural setting – as things are and not as it ‘should be’ in fixed laboratory conditions. They study a situation as an ‘insider’ and interpret events as it is occurring naturally. They understand that context is dynamic, ongoing, and are receptive to changes in the natural environment. Context is given the highest priority in qualitative research.

• Importance for stakeholders – Qualitative researchers understand that the finding of the research applies to people and has social and political implications, so they conduct research more conscientiously. They understand, accept, and acknowledge that qualitative research is storytelling about a phenomenon studied from different perspectives.

These were the few abilities of a qualitative researcher. I did not aim to compare qualitative with quantitative but to highlight the nuances of qualitative research. For further reading, I suggest Denzin and Lincoln 1994 if you are interested to know more about qualitative research. Hope you found it useful 🙂

Hi, welcome back to the systematic review posts. In the last blog post, we discussed the risk of bias assessment of included studies in a review (https://scienceiq.blog/2019/04/18/how-to-do-risk-of-bias-assessment-in-a-systematic-review/). Today, we will move one step further and learn how to do the meta-analysis of the included studies. This post is a step by step tutorial so you can follow each step and perform the analysis as you read.  Before we begin, I would like to give a brief introduction about meta-analysis and when it should be done.

Meta-analysis is a pooled analysis of all the included studies. While you may be analyzing your single study, in the meta-analysis, you will have to pool the results of all the studies and then analyze to get the final result. Now, you may be wondering why we should do a meta-analysis.In literature, few studies may be against a particular intervention, and few may be for it. Then you do meta-analysis and take a final decision about the effectiveness of an intervention based on your results.

It may sound easy, but there are certain criteria which should be followed before considering meta-analysis. Firstly, you should have a homogeneous data, which means that all the studies should have similar outcomes with similar units of measurement. E.g. For pooling data and analyzing gait speed, all the studies should have measured gait speed. If few have measured gait cadence, few have measured balance, and one study has measured gait speed then you won’t be able to do a meta-analysis for gait speed. Secondly, it is advisable to consult a statistician or your supervisors for deciding which outcomes you want to analyze and how you should go about it. There are innumerable ways to analyze the data depending on your objectives.

I have used Review Manager (RevMan) software for the analysis and attached screenshots for each step. Let us see how:

1. Extract all mean values from the included studies in an excel sheet as shown in the picture below.

2. Open RevMan software and choose your review

3. After opening the file, click on the study and references option as shown below and add study under characteristics of included studies

4. Enter all the included studies as shown using first author name and year of publication

5. After adding all the studies, click on add outcome – under data and analyses option

6. Following which, a new comparison wizard opens where you have to click on – add an outcome under new comparison

7. Go to continue and then click on continuous data. You can change it based on the type of your data.

8. Click on next and enter the outcome measure which you are analyzing. Eg. Gait speed

9. Click on next. Depending on the type of analysis you wish to perform, click on either fixed effect or random effect; mean difference or standard mean difference.

10. Go to next and choose your percentage of confidence interval as shown

11. Go to next and label the axis for experimental and control group depending on your outcome measure, whether it is increasing or decreasing.

12. Click on next and click – add study data for the new outcome

13. Continue and choose the studies from which you will add the data. These chosen studies should have the homogeneous data.

14. After adding the studies, click on finish and you will see the following table.

15. Now manually add the post intervention mean and SD values as well as total number of participants for the experimental and control group as shown in the picture

16. You will get the analysis as soon as you enter the data. Click on the forest-plot icon to generate the forest plot

17. This is your final forest plot which you can copy in your text. While reporting, include p-value, confidence interval, heterogeneity and effect size

That’s it! Hope you enjoyed doing the meta-analysis on your own. Feel free to comment if you did not understand any of the steps.

Hello everyone, this post is different from my previous posts on systematic reviews. In this post, I will share those skills that helped me grow as a researcher, and those that I learned/still learning during my PhD. These are a few skills that you should try to acquire while carrying out your research. Hopefully, it will be useful for you and will improve your career prospects 🙂

So let’s begin,

1. Be patient – I was not eager to write this as a first tip, but, it is one of the most important skills for any researcher. I was and still, am very impatient and want to do everything at once. I gradually learned to slow down and give adequate time for everything to fall into its place. Being patient not only helps you to keep your cool but it also improves the quality of your work. Research done in hustle is likely to have more errors and flaws. When you are patiently giving time for something at hand – then you get to think more about the problem and find different ways to solve it.
2. Be passionate – Passion is the fuel for the topsy turvy road of research. If you are not passionate about your research or not ready to undertake the never-ending problems encountered in every research field, then don’t jump into it. Passion will help you face those days when nothing works out, or you get failed at every attempt. With passion and focus, you can overcome the gigantic work required in a PhD.
3. Be curious – Always be keen to learn something new because research methodology keeps evolving and you will have to keep pace with it. There is no ‘one-size fits all’ in research. There will be different study designs, different ways to collect data, new ways of analysis, new ways to approach a problem. Being curious will help you improve your research skills regularly. Try to learn at least one new concept every day, no matter how trivial it is.
4. Keep reading– The more you read, the better you will be. It is not something new, but we often tend to forget it when we get too involved in other deadlines and tasks. If you can’t read every day due to your work schedule, then try to keep one day off only for reading and updating to the latest research in your field. It can be boring, but you can befriend your coffee 😉  
5. Help others– Try to help your friends and juniors as much as possible, I am not saying to leave your work and help them throughout the day but if you get an opportunity to share your knowledge and skills with others then don’t miss it. It will make you very clear about your expertise as well as areas where you need to improve. It will also give you a different perspective of the same situation, which you would not have thought of before. Every time I helped someone in their research work, I have learned something new in the process. 
6. Plan and organize – Of course, this is the most common advice we get as a research student, but still it cannot be understated. Lack of planning and organization will delay your work extensively, and you may end up being demotivated. Poor organization and time management can affect (in a negative way) even the best of research ideas/work. So, know your timeline, follow it and STICK TO IT!
7. Meditate – This sounds a little off-track, but it is one of the most powerful techniques which I learned recently. It significantly reduces your stress level and enhances your focus. If you are not a ‘meditation-type’ person then you can be mindful for a few minutes each day. It gives you clarity of thoughts and sets the tone for productivity.
8. Presentation and public speaking – It is a very common fear which we all have, but good communication is a much-required skill in research fraternity. You should be able to present your work, convince others about its impact and also collaborate if required. These skills develop over time and with practice so don’t be disheartened if you do not have the confidence in public speaking yet. You will learn it if you try!
9. Keep writing – We all are pleased to see well-written articles and wonder if we could ever write like those authors. It is an essential skill to develop as a researcher. It is required everywhere, for protocols, for grants, for publications, for collaboration, so on and so forth. Our words are our biggest strength. So, keep writing until you get it right.
10. Be dedicated – I chose to write it as the last tip so that it stays in your mind for long. You will have answers to all your problems if you are dedicated, not literally, but you get the idea. Dedication will help you climb the ladder of success. It is required to maintain the brilliance of your work and gives you a chance to improve it with every step. So, don’t take it easy and casual.

There are many more skills that you have to develop for becoming a successful researcher. But, I feel that these ten tips are central in the journey of young researchers. I hope you enjoyed reading it.

Hi, welcome back! Hope your reviews are progressing well. If not, then don’t worry and follow each step at a time. Focusing on one step is easier than trying to understand the entire process. In the last post, we discussed data screening and extraction (https://scienceiq.blog/2019/03/23/how-to-screen-articles-and-extract-data-for-a-systematic-review/). Today, we will move on to one of the most fundamental steps of a systematic review, i.e., Assessment of Risk of Bias of the included studies. I will briefly discuss – what is a bias, why it should be assessed, types of biases and how to assess it, with more focus on interventional studies.

What is Bias?

We research to know the truth or reality of the situation. Bias is a deviation from the truth. Example, if a study is not well conducted, then its results may be deviated from the actual reality. Therefore, any methodological flaw in the study may lead to biases in the results or its interpretation.

Why should bias be assessed?

We assess the risk of bias in a study to detect if the results of that particular study are underestimated or overestimated. We do not want to go on face value and need to find the actual truth. If a study has many risks of bias or it is not well-conducted, then it is likely to be reflecting a false/skewed picture. We need to be careful when accepting the results of any study. Especially when we are performing a synthesis of the literature, we need to assess the risk of bias to generalize the findings of the study.

The quality of your systematic review is the quality of studies included in the review. So you need to make sure that the studies included have good quality/fewer biases. If not, then you may either exclude those studies or add a word of caution about interpretation of the results. This is the most important step in a review and requires some experience and understanding of the subject.

What are the different types of biases?

Bias can be present in any form and requires different ways to assess it. Most common biases that may manipulate the results are selection bias, performance bias, detection bias, attrition bias and reporting bias.

Selection bias may be present when the participants in the experimental and control group are not similar at baseline. For example in the experimental group, all the participants are younger than in the control group so the outcomes are automatically better in the experimental group. Such kind of flaw is present due to selection bias. This bias can be minimized by the random selection of participants through random sequence generation and allocation concealment.

Performance bias is present when the participants in one group are more likely to perform an intervention better because of their or investigators’ awareness about the group to which they belong.  Example, if participants in the experimental group receive more care and motivation compared to the control group to achieve additional effect of an intervention. It can be minimized by the blinding of participants or personnel to the intervention and allocation concealment.

Detection bias is similar to performance bias, but it is present due to outcome assessor. If the assessors are aware of the group of participants, then they may measure outcomes differently. Detection bias often exists in the subjective assessment of outcomes. Example, an outcome assessor, may give better scores to the experimental group and poor scores to control group to show more effect of the given intervention. It can be minimized by blinding of outcome assessor to the intervention.

Attrition Bias is present when one group has more attrition/dropouts compared to the other group. The statistical analysis may vary due to the difference in participant number in both the groups which introduces bias. It can be minimized by complete data collection and providing reasons for dropouts in each group.

Reporting bias is present when some of the outcomes are reported while some are skipped. It usually happens when an author reports only the significant outcomes and do not mention about the outcomes that were not significant. It can be minimized by reporting all the outcomes of the study.

How to assess Risk of Bias?

Risk of bias can be measured by looking into the methodology and different domains at risk. Example, in an interventional study, you need to ask questions: were the participants randomly allocated to the experimental or control group? Was there any allocation concealment? Were the outcome assessor and participants blinded? Were all the outcomes were reported? What was the attrition rate in both groups?

Cochrane tool for risk of bias measurement is the recommended tool for interventional studies. For other forms of study design, I have tabulated the commonly used tools. Scales that provide summary scores are NOT recommended for risk evaluation because of the validity of such scales.

Type of study design	ROB Tools
Interventional study	Cochrane Risk of Bias tool, JBI Critical appraisal tool
Diagnostic study	Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Critical Appraisal Skill Program (CASP) JBI Critical appraisal tool
Prognostic/ Prevalence study	CEBM Prognosis Appraisal worksheet JBI Critical appraisal tool CASP cohort study checklist
Accuracy of Outcome Measurement	COSMIN checklist
Qualitative Study	CASP Qualitative checklist JBI Critical appraisal tool

Once you have assessed the risk of bias for each study, summarize the biases in each study, across the different domains, and overall risks present. You can use RevMan to generate appealing tables and graphs for Risk of Bias that you find in Cochrane reviews. You may refer to the following articles if you wish to read further.

1.     BMJ 2011;343:d5928 – The Cochrane Collaboration’s tool for assessing risk of bias in randomised trial

2. Alex Pollock and Eivind Berge, 2018 https://doi.org/10.1177%2F1747493017743796

Welcome back! It’s been a long time since I wrote the posts on systematic reviews. For those who are reading this blog for the first time, I have written previous posts on step by step methods to conduct a systematic review. The last post was on developing the search strategy
and running the search in different databases. (https://scienceiq.blog/2019/01/21/how-to-develop-the-search-strategy-for-a-systematic-review/) In this blog, I will be writing about – how to screen the retrieved articles and to extract the data from included articles.

Screening the articles is easy but one of the most time-consuming tasks in a review so we have technology and software to help us. Let us see in the steps below:

Run the search and retrieve relevant articles:

Once you have run the search in different databases, you will import all the search results in one of the following formats: ris, XML, csv, MEDLINE, etc. (Picture 1) After downloading the results, you will have to remove duplicates. You can use any of the software that I have mentioned below or even Microsoft Excel to remove duplicates, as shown below. (Picture 2 & 3)

Start Screening:

Once you have removed duplicates, you should start screening the articles for inclusion criteria.  It can be done in two ways- manually (which is a herculean task) or using online/offline software. Some of the software which makes life easier are Rayyan (https://rayyan.qcri.org/welcome), Endnote, DistillerSR (https://www.evidencepartners.com/products/distillersr-systematic-review-software/) and RevMan. Get acquainted with any one of the software and start screening the articles by looking at the title and abstract.

I have given an example of screening articles in Rayyan software in the picture below. (Picture 4)

Go to Full-Text Screening:

After excluding articles based on the title and abstract screening, begin full-text screening. When you are not sure about including an article by just looking at the abstract then always save it for full-text screening. Many of my friends are intimidated by the idea of reading manifold articles for full-text screening, but you don’t have to understand the details of each article- screen it by a bird’s-eye view. When screening an article, always follow the PICO format. Eg. First check if population fits in your criteria, if yes then move to intervention/exposure otherwise exclude it.

Population → Intervention → Comparison → Outcome

By using this method, you can easily scroll through the article and not get confused in the process.

Extracting data:

Data extraction depends largely on your research objective. Two people can extract different data from the same article depending on their research question. Cochrane has some data extraction templates. You can modify it according to your objective and always pilot test it using sample articles. Discuss it with your supervisors and get started with data extraction. I am attaching the screenshot of a simple data extraction form. (Picture 5)

What’s next?

I was in a dilemma after completing data extraction. I was unsure about the further steps and did not have the confidence to appraise the articles (because I was not thorough with the included studies.) Once you have successfully extracted the data from all the included articles, try to understand the meaning of the extracted data. Don’t be in a hurry to analyze it. Develop some deeper understanding of your data and try to appraise it critically. Once you have fully understood the included studies and its methodology then go-ahead with the next steps (I will discuss it in the upcoming blogs.)

Tips:

#1- Article screening should be done by two reviewers independently, and any doubts in the decision of including the article should be discussed with the third person or experts.

#2- Using excel file for data extraction will reduce the time and effort of summarizing the articles again.

That’s all for data extraction. We will be moving to data synthesis in the next few blogs. Happy reviewing!

Hello all, welcome back after a long break. Hope you have registered your review in PROSPERO by now. We are now moving to the crucial yet less discussed step of a systematic review, which is developing the search terms. Search terms are the engine of a systematic review. If it is in the right direction, it reaches the destination but if it’s in a wrong direction, it can mislead you, and you might not reach your end-point. The search in a systematic review should be as extensive as possible to ensure that all necessary and relevant articles are included. After all, you are generating the evidence!

Before you run the search and retrieve articles, be careful about what and how many search terms you have used. Ask for help if you are unsure about it. Many of my friends have asked me about it, and I have told the same thing which I am going to tell you now…Using more search terms is not wrong but using fewer terms might lead to missing of some pertinent articles. Don’t be scared to use as many terms as possible in the beginning, and later you can delete the irrelevant terms. Let’s see the stepwise procedure.

• Decide your keywords:

You begin by deciding your keywords. Keywords are the words which summarize your topic. You have to use your PICO terms for deciding your keywords. E.g., if my topic is ‘Effect of TENS on spasticity in adults with stroke’ then my keywords will be ‘TENS’ AND ‘Spasticity’ AND ‘Stroke.’ We also call keywords as concepts.

• Expand each keyword:

Now that you have decided your keywords start growing the terms for each concept/keyword. You can do that by looking for synonyms for each word. Add singular as well as plural words, e.g., activity and activities. You can use variant spellings, e.g., Disc and disk. You may also add other related terms to widen your search.  Truncation and wildcards are other smart ways to retrieve related articles. E.g., for truncation is ‘random*,’ if you use this truncation, then it will search all terms related to random, i.e., random or randomised or randomized or randomly, etc. E.g., for wildcards is ‘wom?n’  so it will include articles with both woman or women. Another simple way to look for synonyms is jotting down all the MeSH terms of the keyword from PubMed and Cochrane. I have attached the pictures on how to search for MeSH terms. You can also add synonym by going to the appendix of any Cochrane review done on a similar topic and writing down the words used in its search strategy.

• Tabulate the search terms:

For clarity and convenience while running a search, tabulate all your keywords as shown below using the same example of keywords as given earlier.

Stroke	TENS	Spasticity
cerebrovascular accidents	transcutaneous electric nerve stimulation	stretch reflex
cerebrovascular disease	transcutaneous nerve stimulation	muscle hypertonia
brain ischemia	transcutaneous electrical neurostimulation	spasm
carotid artery disease	percutaneous neural stimulation	clonus
intracranial embolism	micro amperage electrical stimulation	spastic paresis
intracranial thrombosis	transcutaneous electric stimulation	hypermyotone
intracranial hemorrhage		high tone
brain infarction		hypertonia
vertebral artery dissection		hypertonicity
hemiplegia		hyper reflexia
hemiparesis		hyperreflexia
monoparesis		hyper-reflexia
cerebral vascular disorder		spastic reflex
cerebellar vascular disorder		spastic
brainstem vascular disorders
vertebrobasilar vascular disorders
subarachnoid hemorrhage
intracranial aneurysm
infratentorial hemorrhage,
supratentorial hemorrhage
lacunar stroke
intracranial arteriovenous malformations
brain attack
Final result = R1	R2	R3

• Build your search strategy:

Now build your search strategy by joining the terms under each column with Boolean ‘OR’. Suppose after adding all terms, you get the final result as R1, R2, and R3 for each column respectively, then add all of these with Boolean ‘AND,’ i.e., R1 AND R2 AND R3. You can even add ‘NOT’ if you want to exclude a few terms specifically. See the example in the picture below.

• Running the search:

Once you have completed the herculean task of developing the search terms, you can go ahead with running the search in different databases. You should also include conference abstract and other grey literature. Another important source which may have potential articles are trials registers of ongoing and unpublished trials.

• Few tips:

Keep a record of all the databases searched and number of articles retrieved from each database. Save the search strategy and include it in the appendix of your manuscript. Additionally, try to keep a fine balance between precision and comprehensiveness. Which means that you should try to keep your search extensive but not so extensive that you retrieve more irrelevant articles. Don’t worry; it will come with practice and experience. Let me know if you have any muddles while building your search strategy.

Hi all, I wish you have a wonderful New Year. Because it is a holiday season and everyone is in a festive mood, I will keep this post very short and fun to read. This is the third post on systematic reviews, and if you follow all the posts step by step then you will be able to complete your systematic review by the end of this series 🙂

Hope you are ready with your review protocol, if not go ahead and do it now (https://scienceiq.wordpress.com/2018/12/17/how-to-write-the-protocol-for-your-systematic-review/). Next, we move on to registering it on Prospero.

Step 1 – Open the browser:

I have given the link here (https://www.crd.york.ac.uk/PROSPERO/)
Just like any other website, you need to sign up first.

Step 2 –  Go to my PROSPERO

After signing in, go to “my PROSPERO” in the right corner. Then, click on “Register your review now.”

Step 3: Choose your review type

Once you click on register your review, then you need to select your review category, i.e., is your review on human participants or animal model.

Step 4 – Answer a few questions before you go to the registration

The first question will be: whether it is a scoping or literature or mapping review. If you click “YES” then you won’t be able to register further because currently, Prospero is only registering systematic reviews. So, click on “NO.”

Next, keep answering the questions as shown in the picture. You also need to specify if there is another review which exists on the same topic and if so, why do you wish to do it or how different is your review from the other one.

You will need to specify the stage of your review. Don’t worry if all your answers are “not started.” It won’t matter.

Step 5 – Begin registering

Once you have answered all the questions, you can go ahead to the main 40-item registration form. It is exactly like your protocol. So, if you have prepared your protocol, then you can simply copy-paste it here.  You can also save it and keep filling it at your pace. You may also wish to copy the entire form in a word doc and then fill it (it may take more time).

Last few questions have to be filled when you have completed the entire review. So don’t be afraid to leave those sections blank.

That’s it. It is so simple to register your review. Make sure that your PROTOCOL is well-written; then registration is a cake-walk.  I have also linked the pdf on ‘guidance for registering on Prospero’ for more clarity and details. https://www.crd.york.ac.uk/prospero/documents/Registering%20a%20review%20on%20PROSPERO.pdf)

Hope you have a blessed 2019! Stay connected and comment below if you have any queries.

Hi all, welcome back! Hope you have read and enjoyed the previous blog on “How to do a systematic review-10 simple steps for beginners.” In case you missed it, you can still go back and read it.

We are moving to the details of each step involved in a review. Now that you have decided your amazing topic and formulated the research question, it’s time to write down your review protocol. You can get detailed information on PRISMA website (PRISMA Link:  http://prisma-statement.org/Protocols/ProtocolGuidance). But as promised I have simplified the steps which you should follow in writing your review protocol. So, let’s dive in.

• Title:

Make sure that the title of your review should include the word ‘systematic review’ in it. It helps in identifying the article as a systematic review instantly. E.g., the title of my review was “Effect of TENS on spasticity in adults with stroke; A systematic review and meta-analysis.”

• Authors and their contribution:

Before you begin the review or you are at the protocol stage, you should be sure of your team members and how they are going to contribute to it. Divide the work initially for smooth and hassle-free completion of the review. E.g., Author 1 and 2 will screen and extract the data. Author 3 will conduct the meta-analysis or risk of bias; Author 4 will be contacted in case of disagreement, etc.

• Support/sponsor/funder:

Mention the name of your funders or sponsors if you have got support for conducting the review.

• Introduction – rationale and objective:

Prepare a brief introduction for your review with emphasis on the need for conducting the review. Why this review was required, what is already known and what do you wish to know through this review. Is there no evidence or conflicting evidence on that particular topic? Remember that the need should be convincing enough for the readers and editors of course!

• Methods:

As I explained in the last post, you should discuss your methods with your supervisor or senior colleague because it determines the quality of your review. Write down the eligibility criteria, PICO format, study design, setting, time frame, years considered, language, and publication status for including the studies. Also, mention the databases which you planned to search. Whether or not you wish to include grey literature. It is a good idea to prepare the search strategy of at least one database and write it in the protocol.

• Data extraction:

Give a clear picture of how the data extraction will be done. Will you develop and pilot test the extraction sheet or will you use an existing one? It is always better to adapt the data extraction sheet from an existing one which suits your study requirement. We will discuss further on it later.

• Outcomes:

It is the heart of your review. Mention about all the outcomes which you wish to extract and analyze in the study. Which outcomes do you want to exclude and why?

• Critical appraisal/risk of bias analysis:

This is again a critical and unique component of a systematic review. You will have to write down how will you analyze the included studies or how will you do the risk of bias. Mention the scale or tool which you will use for appraising the article.

• Data Synthesis

Give details on how you will summarize the included studies, i.e., narratively or quantitatively. Ideally, you should do both. Qualitative synthesis would consist of the way you wish to present your data. E.g., an effect of a treatment alone or effect of treatment as an adjunct, or effect in the chronic or acute stage, etc. Details on quantitative synthesis/meta-analysis would include describing the summary measures, subgroup analysis and heterogeneity in the data.

• GRADE your review:

You may also wish to do GRADE (Grading of Recommendations Assessment, Development, and Evaluation) to rate the quality of evidence and develop recommendations in guidelines. More info on http://www.gradeworkinggroup.org/

So, these were some core features which you should mention in your protocol. It would not only help you in building your review of good quality, but it will also help you in planning ahead. A good idea is to keep a timetable for completing the review, right from screening the articles to preparing the manuscript. Otherwise, it may get excessively delayed (don’t ask me how much time I took to complete mine. Once you have written the protocol, register it in PROSPERO, and you are good to go! Best of luck 🙂

Reference

1. ShamseerL, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart L, RISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015Jan 2;349(jan02 1):g7647.

In this blog, you will know more about ‘how to do scientific research.’ Useful for people who are beginning their research journey