Lupine Publishers | Advancements in Cardiovascular Research
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Opinion
Professor Peter Bacchetti’s excellent article [1], highlighting
“the other problem of peer review of finding flaws that are not
really there based on unfounded statistical criticism, and its demoralizing
effect on authors”. I wish to add some thoughts to the
debated issues. Professor David Horrobin’s original classics on the
subject [2,3]. have not yet been surpassed. It was updated recently
[4] and prompted some contributory thoughts [5]. Having enough
experience as author of reject articles and some as peer reviewer,
I find the most devastating effect to author’s morale is making no
comment, giving no reason for rejection or not replying all. The BMJ
is guilty on this account as an article of mine was rejected that was
accepted elsewhere after minor editing [6]. The BMJ, however, is
in the good company of most biomedical journals who apply the
COPE rules. The article lacked statistics of any kind that perhaps
might be one of the reasons it was disliked at BMJ. To Editors’
credit, however, it took about a month to say ‘No’ that caused no
momentum loss, unlike other Journals who reach the same verdict
on other articles after 6 months or a year that drag another year
or two before the author could recover and gather enough time,
interest and energy to face the damn thing again. One subtle aim
of that article [6], mentioned to BMJ Editors, was an attempt to say
that “there is science and in particular evidence based medicine
without statistics”.
It is a devil’s advocate to say statistics has not only been made
into a “big lie” but also ‘false God’. It was invented elsewhere but
currently worshiped only at most medical and surgical journals.
A look at Science and Nature testifies such prestigious magazines
have reduced statistics to real size and value as a “tool for testing
a hypothesis”. It is not too basic a question for every biomedical
peer reviewer to find out the exact role, aim and limitations of
statistics. Some was mentioned in an article [7], nobody noticed
save the late great Professor GD Chisholm editor of Br J Urology.
It was based on a study that was rejected by a grant committee. It
aimed at resolving 2 of the most serious puzzles of current clinical
practice, postoperative hyponatraemia and the multiple vital organ
dysfunction or failure syndrome [8]. However, giving data and
statistics [7,8]. before clarifying the theories [9]. has proved as
wrong as putting the cart in front of the horse. Einstein’s methods
on proposing the special and general relativity theory is the correct
way. When statistics was haled in the sixties everyone thought it
was the only mean to discover “The Unifying Theory”.
This has proved both immensely costly and wrong. The
basic fact is ‘statistics cannot, was not intended to and will never
could, make a discovery’. Observation, mental experiments and
the X factor are the only way to make a discovery long before it is
verified and proved by practical studies and statistical tests. Before
explaining the X-factor please allow me tell a relevant true story
that symbolizes the current problem with statistics. Two friends of
mine in UK had a disagreement, made a bit on a round of drinks
and decided the first person to enter the hospital club will be the
judge. Guess who did? I did but having no clue on how to resolve
the conflict suggested that a flip of a coin might be the best way.
They agreed also to my condition that while head or tail will
determine the winner among them, if the coin stood on edge the
judge should be the winner of all. It did and I won. Another conflict
started on: Who should buy the 3rd round of drinks? Both agreed
that it was my turn. I explained that buying the 3rd round will gain
good company but lose all winnings, and my turn should be the
5th round! The point is statistics can tell the probability of head or
tail and exclude the odd but when evaluating to either 0 or 100%
and the truth is known, instead of expiring it generates residual
arguments. Professor Richard Smith contributed to this debate by
quoting Dr Hedge on Professor Robert Fox’s famous thought that
“swabbing the rejects with the accepts does not make a difference.”
He added that perhaps it has already been done at BMJ” and
asked “How can you know?” With due respect Sir, I frankly think
nobody can. Despite a proven incremental value of an average
article it does not make a noticeable difference or great loss to
scientific advances. Statistically speaking that means a quality
article submitted to BMJ has 50% chance of being accepted or
rejected. So, why not save everybody the trouble and toss a coin?
Here is where statistics has shot itself in the foot. It gives an average
chance to the average and an odd chance to the odd but can’t tell
which is important. The odd chance of a tossed coin to stand on
edge matches that of a breakthrough scientific or medical article
coming an editor or peer reviewer’s way but detecting such article
makes all the difference. Some call it a hunch or gut feeling. Others
qualify it by the three-pronged tests of quality, relevance and
civility. Identifying the “X-factor” that makes such an article stand
out is worth all the trouble. I honestly do not know but it is the
arresting beauty found in Einstein’s famous papers, Newton’s laws,
Mozart’s music and Shakespeare’s writing among many examples
that include medicine [2-4]. I wrote 2 articles on such para-scientific
para-medical stuff to identify the X-factor, “Rules and lures of the
science game” and “The Mozarts of Science” sent to journals nearly
two years ago and have not received a reply yet. I think a message
of “Ignore the big headed bustard” arrived. Qualified people to
find out the X-factor are COPE members. Another question that
requires a ‘Yes’ or ‘No’ answer would be: if any of Einstein’s papers
is evaluated using the current peer review standard and statistics
adopted by most biomedical journals, would it be accepted?
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