The pipe is one of those things that once you've tried it you never want to go back.
The pipe came to R a few years back and I would argue that it is one of the best features. I admit that data munging is a very stepwise process but I find myself frequently missing it in Ts when I code. It makes the code denser as you use fewer intermediate variables while the code id still easy to debug.
The package.json should be able to actively ignore vulnerability id's. As id's disappear with audits the npm audit could just remove those, eg a "prune"
I love the new tabs, I found the old ones clumsy and ugly. Design is unfortunately something that is constantly a moving target. If Mozilla stops updating they will surely die - for most users the technical details don't matter, what matters is that their friends go, wow what is the browser you're using? And thus, redesign is just a way of staying alive...
There is though a lot of good research coming out of the US but from my understanding they can be rather expensive. Sweden has probably some of the best hip arthroplasty outcomes but the cost of these is roughly 5-10 times lower than in the states. I think the system is somewhat stacked against you, if doctors (also human beings) spend 80-100 hours/week during their residency, they expect a financial reward afterwards or no-one would agree to that.
Orthopedics is also somewhat of an odd medical specialty with lots of incentive issues and a scarcity of good clinical trials. In a study from Sydney they found that among the 50% of the surgeries that had been evaluated with randomized clinical trials (RCT), only in 50% of the cases the trials actually supported the surgery. It is though much more difficult to randomize people to surgery, doing the blue or red pill is much easier.
I'm pretty sure also that a big part of the problem is us doctors failing to identify if a patient is part of the long tail or not, i.e. does he/she not fall into a particular study's inclusion criteria or not? Hopefully we will be able to shortcut this problem with deep learning tools but as most self-driving-car enthusiast know - there is a big difference driving around in the parking lot from the open streets.
I'm an orthopedic surgeon with over two decades of coding experience and the last six years I've been working on developing deep learning models for muskuloskeletal images. Despite being in a rather narrow field, only focusing on trauma radiographs, the complexity of the task is hard to overstate. We have piloted our software with our radiologists at the hospital, but the amount of features you need so that the clinicians actually feel that there is any benefit, are huge. At the moment we're training 480+ different labels and I still think we're not even half way for trauma radiographs.
While I knew the concepts of log I first truly understood it when I started looking at everything as log2. In hindsight I feel like a moron, obviously it is about doubling and halving risks is what it's about (I'm an MD so everything is a risk ratio in my field).
I remember a colegue that was presenting results during their defense of a thesis where one risk ratio 0,45 and the other 2,1. I asked which effect was biggest and they automatically replied 2,1. I'm pretty sure that 80%+ of my colegues would make the same mistake. Everyone understands double/half - we should try to teach people this as the word log is just too intimidating for so many.
Please stop viewing health care as one single entity. It is equal to saying that all IT is the same. It is a huge beast that quite often lacks margins.
The pipe came to R a few years back and I would argue that it is one of the best features. I admit that data munging is a very stepwise process but I find myself frequently missing it in Ts when I code. It makes the code denser as you use fewer intermediate variables while the code id still easy to debug.