The British Medical Journal provides an editorial from Professor David Healy, Head of Psychiatry at the Hergest psychiatry unit in Bangor in which it is stated:
When concerns emerged about tranquilliser dependence in the early 1980s, an attempt was made to supplant benzodiazepines with a serotonergic drug, buspirone, marketed as a non-dependence producing anxiolytic. This flopped. The lessons seemed to be that patients expected tranquillisers to have an immediate effect and doctors expected them to produce dependence. It was not possible to detoxify the tranquilliser brand.
Instead, drug companies marketed SSRIs for depression, even though they were weaker than older tricyclic antidepressants, and sold the idea that depression was the deeper illness behind the superficial manifestations of anxiety. The approach was an astonishing success, central to which was the notion that SSRIs restored serotonin levels to normal, a notion that later transmuted into the idea that they remedied a chemical imbalance. The tricyclics did not have a comparable narrative.
In the 1990s, no academic could sell a message about lowered serotonin. There was no correlation between serotonin reuptake inhibiting potency and antidepressant efficacy. No one knew if SSRIs raised or lowered serotonin levels; they still don’t know. There was no evidence that treatment corrected anything.
This lack of evidence-based practice was apparent to Thomas Insel, Director of the US National Institute Of Mental Health who announced in 2013 that the institute would abandon funding towards the DSM:
While DSM has been described as a "Bible" for the field, it is, at best, a dictionary, creating a set of labels and defining each. The strength of each of the editions of DSM has been "reliability" - each edition has ensured that clinicians use the same terms in the same ways. The weakness is its lack of validity. Unlike our definitions of ischemic heart disease, lymphoma, or AIDS, the DSM diagnoses are based on a consensus about clusters of clinical symptoms, not any objective laboratory measure.
In the rest of medicine, this would be equivalent to creating diagnostic systems based on the nature of chest pain or the quality of fever. Indeed, symptom-based diagnosis, once common in other areas of medicine, has been largely replaced in the past half century as we have understood that symptoms alone rarely indicate the best choice of treatment.
Does this mean that psychiatry is finally moving away from a practice akin to leeches for everything?
(Score: 4, Insightful) by Sir Finkus on Friday April 24 2015, @04:07AM
No. The problem is more fundamental than that. The root cause is that psychiatry as a whole is unwilling to see man as anything more than a biological machine.
Is there any evidence that we are not?
I don't see how we solve the problem by discarding science altogether.
Join our Folding@Home team! [stanford.edu]
(Score: 3, Interesting) by HiThere on Friday April 24 2015, @05:49PM
Whether it's reasonable to see a human as a machine depends on how you think of machine. As a programmer, I find it a good model, but then my model of what a machine is is a lot more detailed and complex (and less "mechanical") than that of most people. Perhaps as cars become more autonomous more people will understand that model "correctly" enough that it won't be a problem. Currently people seem to have a model of "machine" that is more deterministic than a Pachinko machine.
(Score: 0) by Anonymous Coward on Saturday April 25 2015, @03:28AM
A computer is merely a machine, but if Microsoft Exchange isn't running properly it's usually not helpful to fix it by looking at the problem from a Computer Engineering point of view or even an Electronic Engineering or pure Math point of view.
I doubt with our current technology and scientific knowledge we are able to even 99.99% simulate a single cell difflugia or a white blood cell. They may just be machines, but how much do we really know and understand about how they work that we can say we can build an _equivalent_ thing (in a simulator or for real) instead of merely creating a simplified model of one. You can make a simplified model of Stephen Hawking that appears 99.99% accurate for most scenarios (like his daily movements) but it's 0% accurate for some important scenarios ;).