Decision Tree Questions In Genetics And The USMLE

Courtesy cayusa@flickr. (creative commons by-nc license)

Just a quick thought. It just occurred to me that some of the questions on the USMLE involving pedigree analysis in genetics, are actually typical decision tree questions. The probability that a certain individual, A, has a given disease (eg: Huntington’s disease) purely by random chance is simply the disease’s prevalence in the general population. But what if you considered the following questions:

• How much genetic code do A and B share if they are third cousins?
• If you suddenly knew that B has Huntington’s disease, what is the new probability for A?
• What is the disease probability for A’s children, given how much genetic code they share with B?

When I’d initially written about decision trees, it did not at all occur to me at the time how this stuff was so familiar to me already!

Apply a little Bayesian strategy to these questions and your mind is suddenly filled with all kinds of probability questions ripe for decision tree analysis:

• If the genetic test I utilize to detect Huntington’s disease has a false-positive rate x and a false-negative rate y, now what is the probability for A?
• If the pre-test likelihood is m and the post-test likelihood is n, now what is the probability for A?

I find it truly amazing how so many geneticists and genetic counselors accomplish such complex calculations using decision trees without even realizing it! Don’t you ?

Copyright © Firas MR. All rights reserved.

A Force Weaker Than Gravity?

Courtesy laurenatclemson @ Flickr (attribution license)

Just thinking aloud a question that’s been ringing in my head recently. Gravity is the weakest force that we know of. In flapping its tiny wings, a fly easily overcomes the gravitational pull of this gigantic earth that we inhabit. A massive airplane can carry hundreds of people on board as it cruises the skies.

But what is it that makes gravity so weak? I think the secret lies in the gravitational constant. What if there’s a force out there whose constant(s) make it so weak that we just haven’t experienced its direct effects yet? A force weaker than gravity?

Could the Higgs field be a candidate for what I’m thinking about?

Copyright © Firas MR. All rights reserved.

Elegance In Inelegance

Courtesy Lydia Elle @ Flickr (by-nc license)

I just finished a great lecture series on the history of mathematics by Dr. David Bressoud recently¹. Remember how I once spoke about elegance in inelegance? How some people have argued (eg: Lee Smolin) that the universe just might be complex by nature? How mankind might just be wrong about looking for simple and thus elegant solutions to explain physical phenomena?

Well, I was pretty intrigued by some of the stuff I learned about Henri Poincare’s work in this regard. Poincare is famous for a number of things, his Poincare conjecture being the most obvious of them. A Russian math guru, Grigori Perelman, apparently proved this conjecture some years back and among other peculiar things, not only declined the Fields medal but also a million dollar prize for solving one of the toughest math problems ever known.

But I was particularly piqued by how Poincare was fascinated by this idea of finding elegance and hidden patterns even where one might expect junk. Here are what might be interesting questions as crude examples:

Take a random set of 100 beads. Throw these beads on the floor. They scatter randomly. How many throws would be needed to find at least three beads on the floor that yield an equilateral triangle when they are connected? How many throws would you need to find a cluster of beads that is of a certain shape or size?

That there is some sense of order even in randomness and chaos, is truly an enchanting concept.

Have any thoughts of your own? Do send in your feedback !
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1. Queen Of The Sciences (Lectures by David Bressoud)
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Copyright © Firas MR. All rights reserved.

The Story Of Sine

zaveqna@flickr (by-nc-sa license)

I’ve been studying mathematics lately and really enjoying it. Here’s an interesting story about the history of the trigonometric function, ‘sine‘.

Early in the 1st millenium A.D., a new way of thinking about chords was coming about. The chord is defined as the straight line that joins two points on the circumference of a circle. The ancient Greeks had developed trigonometric functions to calculate the length of arbitrary chords. But several centuries later, by the early 1st millenium A.D., mathematicians in India began to think about calculating and working with half-chord lengths instead. For this, they developed the familiar ’sine’ and ‘cosine‘ functions that we still use to this day. The earliest accounts of the use of the half-chord in Indian texts, is from the Surya Siddhanta (c. 300 – 400 AD), written in Sanskrit. The sound of the Sanskrit word used for ‘half-chord’ was ardha-jya [ardha = half, jya = chord]. Perhaps they found this word too long and eventually it was shortened to jya or jiva for all practical purposes.

By roughly the end of the 1st millenium A.D., the vanguard of scientific growth was now in the hands of the Arab world. In translating the works from Sanskrit into Arabic, scholars in the Arab world transliterated and pronounced jiva as jiba [جب]. The sound ‘jiba‘ is recorded in Arabic as two consonants j [ج] and b [ب] with no vowels explicitly written between them. The vowel sounds are merely implied.

Several centuries later, after the decline of scientific growth in the Arab world, came the Europeans. When they in turn came upon the Arabic word for jiva and tried to translate it, they of course ended up with a word, ‘jb‘ [pronounced as 'jay bee']. Apparently, they were oblivious of the implied vowel sounds. Things were dandy for the Arab scientists, but the Europeans couldn’t make any sense of the sound ‘jay bee‘ because such a sound doesn’t exist in any of the words in the Arabic language. They found that the closest sound to ‘jay bee‘, was the sound ‘jaib‘ or ‘ja-eeb‘, in the Arabic word for the mammary gland! And so the Europeans assumed that the half-chord was to be referred to with a Latin word that meant mamma, mammary gland or any of its other synonyms. Perhaps out of modesty, it was ultimately instead decided that the word used for the fold of a cloth utilized to cover a mamma would be appropriate to refer to a half-chord. This word was ’sinus’. And from this Latin word ‘sinus‘, ultimately came the English word ‘sine‘ that is in use today!

Remarkable, isn’t it?

Feel free to send in your feedback, corrections and comments .

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References:

1. Queen Of The Sciences (Lectures by David Bressoud)

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Copyright © Firas MR. All rights reserved.

Wilson!

Remember Wilson the volleyball, from the movie ‘Castaway‘? In a dash of newfound creativity and imagination, I shot a couple of photographs today of a coconut that shared an uncanny resemblance to Wilson! Seemed like the perfect opportunity for some prop-photography .

Wilson reads for the USMLE.

Somehow he seems quite absorbed with the book , don’t you think? Ah, nothing beats the joy of laying back, relaxing and reading a nice book.

Wilson in the Orient.

On his travels to the Orient, he received a fancy hand-held fan as a gift from a monk. In fact, that is exactly where he discovered his inner intellectual .

Wilson is a bike rider ...

… and loves his moped. How else would an intelligent coconut choose to conquer the streets?

Copyright © Firas MR. All rights reserved.

Keywords For Your Surgical Rotation In Med School

An Ongoing Surgery

Bonjour everyone! Today, I’m going to share with you some high yield keywords that should hopefully help you breeze through your surgical rotations in med school. Call it a checklist if you will. The objective is to facilitate memory recall and help you gear up with areas that you just have to familiarize yourself with, ideally before the start of your rotations. Understand that these are just keywords, with a special emphasis on surgical instruments, and you’ll really need to read some good books to develop your knowledge base. For a rapid-fire review I suggest Surgical Recall. For basic surgical skills, you might like RM Kirk’s Basic Surgical Techniques. It is also a good idea to refer to specific sections (for pictures of incisions, instruments, etc.) of a good reference book on the surgical specialty you’ll be rotating in. Finally, like we all know, surgery is an area that is incredibly skill based and different people have different preferences when carrying out the same thing – be it tying a knot, controlling a bleeder or what have you. You’ll learn to modify the way you do things depending on the specific ways of your surgical team.

I’ve also interspersed keywords specific to two areas that I have an interest in with regards to surgery, or rather surgical oncology to be exact – general thoracic surgery and colorectal surgery.

I shall be updating this list as the need comes. Comments, corrections and feedback are always welcome! Bye for now !

Copyright © Firas MR. All rights reserved.

On The Impact Of Thinking Visually

Romanesco broccoli - One of many examples of fractals in nature. (Wikipedia)

What do Mandelbrot and Einstein have in common?

They were/are both math aficionados. But more importantly, they both laid down the foundations for thinking about abstract natural phenomena in a geometrical way. The impact was reverberating.

Before Einstein came along, people had no real sense of gravity at all. Yea sure, there was Newton’s universal law of gravitation. But no one really could make any sense whatsoever of how exactly gravity might operate. Was it a wave? If so, at what speed could it act? Was there something particulate about it? Gravity was so mystical. And as always, so have been the concepts of time and space. Einstein’s greatest achievement in my view is that not only was he able to lay out the underpinnings of such phenomena in the form of a couple of abstract equations, but perhaps more importantly, that he devised a method to think about them visually. In developing his theories of special and general relativity, Einstein proposed the idea of the space-time fabric. It has a 3-D structure, yet represents four dimensions – 3 in space and 1 in time. Gravity would result from distortions in this fabric. The speed with which gravity could influence an object would depend on how fast these distortions could travel. And this central notion of ‘distortions in a fabric’ would also influence our understanding of the more difficult to grasp concepts of time and space. Time and space could mean different things to different observers depending on how this fabric was warped or sliced.

Mandelbrot achieved the same thing with his theory of fractals. How can complex natural structures and phenomena be represented mathematically? How to mathematically model a plant, the form of a human or a mountain range? In spite of how incredibly difficult it all sounds, these complex shapes could all be simplified into repeating units of tiny yet geometrically simple components – fractals. Mandelbrot went on to write his epic, “The Fractal Geometry Of Nature” and there was no turning back. Suddenly so many of nature’s workings could now be analyzed mathematically. An immensely significant step for mankind indeed. What I find absolutely fascinating about fractals, is the discovery that many intangible natural phenomena also contain a fractal component. Dr. Ary Goldberger and his team of researchers at Harvard Medical School have been working on applying fractal theory to medicine and biology. For those of you who might not be familiar with Dr. Goldberger, the name might ring a bell if you’ve read his books on electrocardiography. For Dr. Goldberger, interest in electrocardiography runs in the family, his father having invented the augmented limb leads back in the day. Among some of the things I learned about his work on electrocardiography, is that his team has shown that there is a fractal nature to ECG waveforms! This isn’t something like representing the heart itself in fractal form. It’s the activities of the heart that we are talking about here. Something really quite abstract. By looking at these fractal patterns, one could potentially detect pathology at a much earlier stage. Fractal patterns and their aberrations could help detect diseases in ways that no one had ever imagined! If you want to dig what’s cool, check out what’s been going on in the world of fractals in medicine – from human vasculature, to the brain and beyond. A quick PubMed query would lead you to a lot of riveting literature on the topic. Don’t forget to also take a look at the excellent documentary on fractal theory from PBS NOVA, “Hunting The Hidden Dimensions“.

Copyright © Firas MR. All rights reserved.

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Why Equivalence Studies Are So Fascinating

Bronze balance pans and lead weights from the Vapheio tholos tomb, circa 15th century BC. National Museum, Athens. Shot courtesy dandiffendale@Flickr. by-nc-sa license.

Objectives and talking points:

• To recap basic concepts of hypothesis testing in scientific experiments. Readers should read-up on hypothesis testing in reference works.
• To contrast drug vs. placebo and drug vs. standard drug study designs.
• To contrast non-equivalence and equivalence studies.
• To understand implications of these study designs, in terms of interpreting study results.

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Howdy readers! Today I’m going to share with you some very interesting concepts from a fabulous book that I finished recently – “Designing Clinical Research – An Epidemiologic Approach” by Stephen Hulley et al. The book speaks fairly early on, on what are called “equivalence studies”. Equivalence studies are truly fascinating. Let’s see how.

When a new drug is tested for efficacy, there are multiple ways for us to do so.

A Non-equivalence Study Of Drug vs. Placebo

A drug can be compared to something that doesn’t have any treatment effect whatsoever – a ‘placebo’. Examples of placebos include sugar tablets, distilled water, inert substances, etc. Because pharmaceutical companies try hard to make drugs that have a treatment effect and that are thus different from placebos, the objective of such a comparison is to answer the following question:

Is the new drug any different from the placebo?

Note the emphasis on ‘any different’. As is usually the case, a study of this kind is designed to test for differences between drug and placebo effects in both directions¹. That is:

Is the new drug better than the placebo?

OR

Is the new drug worse than the placebo?

The boolean operator ‘OR’, is key here.

Since we can not conduct such an experiment on all people in the target ‘population’ (eg. all people with diabetes from the whole country), we conduct it on a random and representative ’sample’ of this population (eg. randomly selected diabetes patients from the whole country). Because of this, we can not directly extrapolate our findings to the target population without doing some fancy roundabout thinking and a lot of voodoo first – a.k.a. ‘hypothesis testing’. Hypothesis testing is crucial to take in to account random chance (error) effects that might have crept in to the experiment.

In this experiment:

• The null hypothesis is that the drug and the placebo DO NOT differ in the real world².
• The alternative hypothesis is that the drug and the placebo DO differ in the real world.

So off we go, with our experiment with an understanding that our results might be influenced by random chance (error) effects. Say that, before we start, we take the following error rates to be acceptable:

1. Even if the null hypothesis is true in the real world, we would find that the drug and the placebo DO NOT differ only 95% of the time, purely by random chance. [Although this rate doesn't have a name, it is equal to (1 - Type 1 error)].
2. Even if the null hypothesis is true in the real world, we would find that the drug and the placebo DO differ 5% of the time, purely by random chance. [This rate is also called our Type 1 error, or critical level of significance, or critical α level, or critical 'p' value].
3. Even if the alternative hypothesis is true in the real world, we would find that the drug and the placebo DO differ only 80% of the time, purely by random chance. [This rate is also called the 'Power' of the experiment. It is equal to (1 - Type 2 error)].
4. Even if the alternative hypothesis is true in the real world, we would find that the drug and the placebo DO NOT differ 20% of the time, purely by random chance. [This rate is also called our Type 2 error].

The strategy of the experiment is this:

If we are able to accept these error rates and show in our experiment that the null hypothesis is false (that is ‘reject‘ it), the only other hypothesis left on the table is the alternative hypothesis. This has then, GOT to be true and we thus ‘accept’ the alternative hypothesis.

Q: With what degree of uncertainty?

A: With the uncertainty that we might arrive at such a conclusion 5% of the time, even if the null hypothesis is true in the real world.

Q: In English please!

A: With the uncertainty that we might arrive at a conclusion that the drug DOES differ from the placebo 5% of the time, even if the drug DOES NOT differ from the placebo in the real world.

Our next question would be:

Q: How do we reject the null hypothesis?

A: We proceed by initially assuming that the null hypothesis is true in the real world (i.e. Drug effect DOES NOT differ from Placebo effect in the real world). We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis. We do this with the belief that such a conclusion is within our pre-selected margin of error. Our pre-selected margin of error, as mentioned previously, is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject‘ the null hypothesis and conclude that a difference in effect has not been proven⁴.

A lot of scientific literature out there is riddled with drug vs. placebo studies. This kind of thing is good if we do not already have an effective drug for our needs. Usually though, we already have a standard drug that we know works well. It is of more interest to see how a new drug compares to our standard drug.

A Non-equivalence Study Of Drug vs. Standard Drug

These studies are conceptually the same as drug vs. placebo studies and the same reasoning for inference is applied. These studies ask the following question:

Is the new drug any different than the standard drug?

Note the emphasis on ‘any different’. As is often the case, a study of this kind is designed to test the difference between the two drugs in both directions¹. That is:

Is the new drug better than the standard drug?

OR

Is the new drug worse than the standard drug??

Again, the boolean operator ‘OR’, is key here.

In this kind of experiment:

• The null hypothesis is that the new drug and the standard drug DO NOT differ in the real world².
• The alternative hypothesis is that the new drug and the standard drug DO differ in the real world.

Exactly like we discussed before, we initially assume that the null hypothesis is true in the real world (i.e. the new drug’s effect DOES NOT differ from the standard drug’s effect in the real world). We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis – with the belief that such a conclusion is within our pre-selected margin of error. Just to repeat ourselves here, our pre-selected margin of error, is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject’ the null hypothesis and conclude that a difference in effect has not been proven⁴.

An Equivalence Study Of Drug vs. Standard Drug

Sometimes all you want is a drug that is as good as the standard drug. This can be for various reasons – the standard drug is just too expensive, just too difficult to manufacture, just too difficult to administer, … and so on. Whereas the new drug might not have these undesirable qualities yet retain the same treatment effect.

In an equivalence study, the incentive is to prove that the two drugs are the same. Like we did before, let’s explicitly formulate our two hypotheses:

• The null hypothesis is that the new drug and the standard drug DO NOT differ in the real world².
• The alternative hypothesis is that the new drug and the standard drug DO differ in the real world.

We are mainly interested in proving the null hypothesis. Since this can’t be done⁴, we’ll be content with ‘failing to reject’ the null hypothesis. Our strategy is to design a study powerful enough to detect a difference close to 0 and then ‘fail to reject’ the null hypothesis. In doing so, although we can’t ‘prove’ for sure that the null hypothesis is true, we can nevertheless be more comfortable saying that it in fact is true.

In order to detect a difference close to 0, we have to increase the Power of the study from the usual 80% to something like 95% or higher. We wan’t to maximize power to detect the smallest difference possible. Usually though, it’s enough if we are able to detect the the largest difference that doesn’t have clinical meaning (eg: a difference of 4mm on a BP measurement). This way we can compromise a little on Power and choose a less extreme figure, say 88% or something.

And then just as in our previous examples, we proceed with the assumption that the null hypothesis is true in the real world. We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis – with the belief that such a conclusion is within our pre-selected margin of error. And to repeat ourselves yet again (boy, do we like doing this ), our pre-selected margin of error is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject‘ the null hypothesis and conclude that although a difference in effect has not been proven, we can be reasonably comfortable saying that there is in fact no difference in effect.

So Where Are The Gotchas?

If your study isn’t designed or conducted properly (eg: without enough power, inadequate  sample size, improper randomization, loss of subjects to followup, inaccurate measurements, etc.)  you might end up ‘failing to reject’ the null hypothesis whereas if you had taken the necessary precautions, this might not have happened and you would have come to the opposite conclusion. Purely because of random chance (error) effects. Such improper study designs usually dampen any obvious differences in treatment effect in the experiment.

In a non-equivalence study, researchers, whose incentive it is to reject the null hypothesis, are thus forced to make sure that their designs are rigorous.

In an equivalence study, this isn’t the case. Since researchers are motivated to ‘fail to reject’ the null hypothesis from the get go, it becomes an easy trap to conduct a study with all kinds of design flaws and very conveniently come to the conclusion that one has ‘failed to reject’ the null hypothesis!

Hence, it is extremely important, more so in equivalence studies than in non-equivalence studies, to have a critical and alert mind during all phases of the experiment. Interpreting an equivalence study published in a journal is hard, because one needs to know the very guts of everything the research team did!

Even though we have discussed these concepts with drugs as an example, you could apply the same reasoning to many other forms of treatment interventions.

Hope you’ve found this post interesting . Do send in your suggestions, corrections and comments!

Adios for now!

Copyright © Firas MR. All rights reserved.

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1. An alternative hypothesis for such a study is called a ‘two-tailed alternative hypothesis‘. A study that tests for differences in only one direction has an alternative hypothesis that is called a ‘one-tailed alternative hypothesis‘.
2. This situation is a good example of a ‘null’ hypothesis also being a ‘nil’ hypothesis. A null hypothesis is usually a nil hypothesis, but it’s important to realize that this isn’t always the case.
4. Note that we never use the term, ‘accept the null hypothesis’.

What You Might Not Know About Scientific Journals

A reviewer at the National Institutes of Health evaluates a grant proposal. (Wikipedia)

I managed to read quite a number of interesting books in the last couple of months. Among them, was Scientific Writing: Easy When You Know How by Jennifer Peat et al. Marvelous book and one that I highly recommend. The book has been mainly written for health professionals. It gives you an insider’s view of how the entire peer(expert)-review process in scientific publishing works. There are also interesting nuggets on peer-review outside of medical journals such as conferences, scientific meetings, etc.

The publishing process in a nutshell:

1. Upon submission to a journal, a paper will first go through preliminary screening by special staff who check for typographical errors. Not scientific merit. Did you stick to the word limit? Are the margins, fonts and spaces in accordance with the journal’s ‘instructions to authors‘ policy? If not, the paper will bounce back like rejected email!
2. If it does scrape through, it goes to an editorial committee. Editors in turn run an ambiguous check on the paper’s scientific rigor and impact, whether it appeals to their sensibilities and whether it makes business sense to get it out in their journal. It is then forwarded to external reviewers.
3. Many journals maintain databases of potential external reviewers who are ‘experts’ in their fields, some of whom are on contract for the journal and others who are not. These reviewers have a track record of being active in other journals and meetings. Journals may even rank reviewers based on whether they review papers on time, their general demeanor with authors of papers, etc. Often these chaps are perched in just about every nook and corner of the world. They look at the paper’s strengths and weaknesses in terms of study design, whether the conclusions put forth are in accordance with the reported results, whether the statistics measure up, whether certain areas need clarification, whether some parts should be rephrased or even omitted altogether. Their comments and annotations are then forwarded to the editors and in turn to the authors.
4. Both editors and reviewers often refer to checklists to standardize this process, even if it be somewhat ambiguous. Because different people have different mental cutoffs for ‘clinical significance’ when it comes to reported results, different people will reach different conclusions even if they look at the same ’statistically significant’ data. When two reviewers differ in what they think about a paper, editors will often request a third reviewer to look at it.
5. After a lot of back and forth communication between authors, editors and reviewers the paper is finally published. The editorial committee is the final arbiter that decides whether or not the paper gets published.
6. This process usually take months, unless there is a good reason.

Here are some interesting facts that you might not know about scientific journals:

1. Multiple surveys have shown that journals are more likely to publish ’statistically significant’ findings. This is an important thing to realize. For any scientific study with a Type 1 error rate of 5%, if the null hypothesis was true you would get a statistically significant result 5% of the time. Purely as a result of random chance. But it’s the 5% of studies that report such a ’statistically significant’ result that are more likely to get published than the remaining 95% of studies that don’t.
2. Most of the scientific literature is biased in favor of content produced in English. Translated works are an extreme minority.
3. The most popular articles in a journal are reviews, editorials, letters, etc. and not research papers. Consequently, journals contain more narrative reviews than genuine research. It’s what keeps them in business.
4. Being published is not necessarily something that is a natural consequence of your scientific caliber or contribution to mankind. It is a very political and arbitrary thing. Maybe the editors or reviewers for the journal are biased against your work. Or it could be that the editors do not think publishing your paper will increase their business, for obscure reasons. Maybe your paper is just too specialized and caters to a minority niche of readers. Editors usually want stuff that sells and increases readership (who by the way, more often care about narrative reviews as mentioned previously), impact factors and profits. Quite similar to newspapers actually. Editors may even decide to publish a paper regardless of what the reviewers think, as long as it makes sense to them to do so!
5. When you submit a paper to a journal for consideration, you immediately transfer whole and sole copyrights to it. You are not permitted to share that paper outside of the research team without prior permission from the editors. Transfer of copyright to journals is pretty common and there are only a minority of fledgling journals out there that give you the luxury of retaining copyrights.
6. Many journals have pre-publication ‘embargoes’. If you have discussed your paper in a scientific conference, meeting, on a random website, with the press … and so on, different journals will have different policies on whether or not such a paper constitutes ‘duplicate’ material. That depends on how many beans you spilled out during such conferences, talks, … etc. and under what circumstances. Did you discuss just the abstract, some random figures and tables or the whole thing? Did you submit the paper before or after such disclosure? Does it constitute a copyright violation? If it’s considered duplicate, it will not be published unless there is a good reason.
7. Transfer of copyright also means that you cannot submit your paper elsewhere or hand out copies of it to colleagues in meetings, conferences, etc. You can’t show off the paper on a website either. As long as the paper is under consideration for publication, you need prior permission from the journal. If the paper is rejected or withdrawn from submission, the copyrights are transferred back to the authors.
8. Different journals will have different time limits on copyright. Some will allow you to maintain a copy on a website or a repository after a number of years have passed. These can rightly be called post-publication ‘embargoes’².
9. Scientific knowledge is thus ultimately controlled by vested interests making it difficult for a free and open society. This has led to calls for reform in peer-reviewed scientific publishing, including the open-access movement. There are two main models in open-access: Open-access journals, that make all peer-reviewed content free to the public. Journals from the Public Library Of Science (PLoS) are a good example. Open-access self-archives are another model. Authors can deposit copies (a.k.a. ’self-archives’) of pre-prints or post-prints of articles that they have submitted to non-open-access, peer-reviewed journals that agree to such activity. They can then share these self-archives using websites and other tools. However, often self-archives are deposited in repositories which are usually institutional. Such repositories allow free public access not only to peer-reviewed scholarly content, but also non-peer-reviewed content such as theses and other gray literature. OAIster is a good example of a cross-repository search engine¹.
10. In certain cases you may want to submit your research for urgent publishing. Different journals will call these kinds of papers by different names – ‘rapid response’, ‘rapid paper‘ …, etc. Often they do not contain too much detail as to study design or statistical rigor. These papers will be submitted by editors to external reviewers on the condition that they be reviewed within a specified time frame. Once such a paper has been accepted and published, you may not be able to submit an addendum or supplement later as it might be considered ‘duplicate’ material!
11. Following reporting guidelines such as those mentioned at the Equator Network, will improve your chances of being published.
12. Submitting your paper to a specialty journal increases your chances of success. Most papers fulfill a niche and so do most specialty journals.
13. The chances of you being struck by lightning are higher than the chances that your paper will be accepted without modification. Nearly always, editors and reviewers will get back asking you to change your paper in some way.
14. In highly specialized fields, many journals will use the same set of reviewers. If you disagree with a reviewer and choose to withdraw your submission, it will not do you much good to submit to a different journal.
15. Reviewers are usually free to remain anonymous to authors. And some journals will let authors be anonymous to reviewers in the interest of fairness. However, anonymity does not always happen.
16. If you are well known in your field, don’t be surprised if you receive an offer to expert-review a paper from a random journal.
17. Despite how enticing it sounds, reviewers do not make a lot of money from this business!
18. Different journals select editors using different criteria. At the end of the day, it is the business team of a journal that usually decides. A candidate who can improve a journal’s appeal, impact factor and business profits ultimately wins.

Have anything else to share that’s not on the list? Send me your feedback and I’ll put it up here!

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Your feedback counts:

1. Special thanks to Stevan Harnad of Open Access Archivangelism fame for corrections in the comments. Matt Warren writes in to talk about the NIH’s involvement in open-access. Their Pubmed Central service is worth checking out. ^{[go back]}
2. With regards to ‘embargoes’ and copyrights, Christina Pikas writes in to say that most of this stuff is part of the ‘copyright transfer agreement’, which should always be examined carefully. She also says that many institutions can influence how many rights you have and that if your work was done for a corporation, a corporate lawyer will often help you in the process. Just to add a tiny point, the book that I referred to above mentions that many institutions have policies on copyright and intellectual property (IP) for their departments. Some will allow researchers to hold on to IP rights, while others will take over these IP rights from them. It’s always a good idea to check with your institution or department. ^{[go back]}

Copyright © Firas MR. All rights reserved.

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The Beginnings Of A New Era

Cape Lion

It’s been well over a year since I had initially revamped the theme of my blog. At the time, I was mainly interested in improving compatibility with the web browser, Konqueror. The Digg theme worked well for me and I was pretty much happy with it overall. But after a couple of months anything gets boring! One of WordPress’s main strengths is the awesome collection of themes available. Some of these themes trickle down to WordPress’s hosting service, WordPress.com . It’s about time for a brand new theme, don’t you agree ? I now bring to you a completely refreshing look for the blog. Gone is the large image header. The focus is on extreme simplicity and lack of clutter. Even the RSS icon has been done away with completely, leaving only a line of text to click on instead. I’m also sporting a new gravatar – a public domain image that I stumbled upon at Wikimedia Commons and that just absolutely blew me away the first time I saw it. It is a drawing by the Dutch artist Rembrandt Harmenszoon van Rijn of the now extinct Cape Lion (Panthera leo melanochaitus) made circa 1650-52 in Louvre, Paris. I thought, since one of the synonyms of my Arabic name ‘Firas’ (فراس) is ‘Asad’ (اسد) meaning ‘Lion’ in English, it couldn’t get any better! Another feature that I’ve introduced, is a rating system for posts and comments. You’ll find a very simple way to vote – a thumbs up or thumbs down – at the very bottom of each post or comment. However, I request that you please pay some thought before actually voting!

I hope you like the new look. Do send in your feedback. Polls can be meaningless, with multiple votes. But hey, give it a try!

On a side note, I have now entered exam mode. Yet again! Oh well, just another byproduct of living life as a medic I suppose. My last entry was a little long. I think I’ll be cutting down on writing for a while. Fear not though! If there’s something that can’t wait to be written, you’ll hear about it asap !

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