Zugzwang is a German word which translates to "compulsion to move." There comes a time in chess, when moving your piece is fatal to your game, and, not-to-move is not an option that you might otherwise exercise.
Has the Gordian knot of data -- and the compulsion to add more data, sans reflection, sans a salient matrix, sans logic, sans common sense, sans clinical judgment, sans practically everything except unthinking tribute to mathematical numbers and statistics, in the pursuit of a puzzle wrapped in a mystery and placed inside an intellectual riddle besides a scintillating/shimmering mirage -- that is pathognomonic of migraine research, sealed the fate of migraine and its researchers both to zugzwang?
In medicine, and medical research, how you start determines where you will end!! Sometimes, you start in African safari, stare at huge turds, and end-up with your name linked to lymphomas for perpetuity--not in a laboratory.
Is it too late already? Will data indeed overwhelm logic and common sense quintessential to genius? Or will inspiration clear the deck and ring the bell?
I love the allusion to staring at lion turds and lymphomas! The problem with too much data is that one ends up with constipation if one deals with it in a linear thought structure akin to the gut
Headache/migraine is at an exciting stage in intellectual understanding with the explosion of new theories and the collection of enormous quantities of data
I would suggest that the key to processing data is to find a way to recognise the patterns within it. This recognition may start by being more selective about the way that the data is initially collected, but the later filtering process to recognise the patterns is just as important
I am all for the empiricism in its original use of the word " based on, concerned with, or verifiable by observation or experience rather than theory or pure logic " but theory and logic is an integral process that goes hand in hand, and is certainly useful in exploring new ways of thinking.
RCTs have become the by-word for research and proof over the last half century, but they have their weaknesses. The analysis of the data is largely through statistical methods that seek a statistical significance of the averaged majority. This is useful as far as it goes for a single and well defined question (eg does A produce B?) to confirm a theory that is already expounded. However it does not usually in itself help create new ways of thinking.
However all is not lost since to my mind it is the outlying results (the 10% who have absolutely no or absolutely total response) that may indicate new and fruitful areas of research. These responses that do not fit the established pattern allow one to look at things with fresh eyes
Pattern recognition rather than deductive logic is the cornerstone of clinical practice. Perhaps we can integrate more of it into theoretical research models...
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