It Takes a World to End a Pandemic

Great article for discussing the production of scientific knowledge, shared knowledge, and also the new knowledge and technology theme.

Scientific Cooperation Knows No Boundaries—Fortunately

Infectious diseases, it is commonly said, know no borders, and neither does the knowledge needed to fight them. Scientists around the world routinely share information and collaborate across borders.  The current pandemic has scientists working together on platforms such as Slack, and using new tools, such as machine learning, to rapidly detect the novel coronavirus in tests that use large amounts data from multiple sources. This outbreak has demonstrated in real time how scientific understanding can indeed be a global public good.

Who Should Be Saved First? Experts Offer Ethical Guidance

This article gets into a lot of the relevant issues of medical ethics and uses appropriate ethical language in the discussion. I’m working with this in my class today (remotely). Here’s the worksheet I’m using today.

TOK day 53

This also connects to some questions that came up after Hurricane Katrina put a hospital in New Orleans in a situation in which it had to make similar decisions about life and death.

12 Katrina Hospital Ethics

Italians over 80 ‘will be left to die’ as country overwhelmed by coronavirus

Please ignore the sensational headline but the article connects to many discussions that relate issues around ethics and public policy. This is a real life application of a form of “trolley problem” playing out in real life. This goes back to some of the choices faced by a hospital in New Orleans after Hurricane Katrina in 2005. When forced to make decisions about whose lives to save, how do we decide?

“The criteria for access to intensive therapy in cases of emergency must include age of less than 80 or a score on the Charlson comorbidity Index [which indicates how many other medical conditions the patient has] of less than 5.”

The ability of the patient to recover from resuscitation will also be considered.

One doctor said: “[Who lives and who dies] is decided by age and by the [patient’s] health conditions. This is how it is in a war.”

An algorithm that can spot cause and effect could supercharge medical AI

How do we make better use of this piecemeal information? Computers are great at spotting patterns—but that’s just correlation. In the last few years, computer scientists have invented a handful of algorithms that can identify causal relations within single data sets. But focusing on single data sets is like looking through keyholes. What’s needed is a way to take in the whole view.

How to Counter the Circus of Pseudoscience

“That is also the case for other health professionals whose practice is based on science, like qualified dietitians, physiotherapists, occupational therapists and psychologists. Guidelines are revised, advice is reversed — on blood pressure, diet, hormone replacement, opioid prescribing. This can be immensely frustrating for patients, even though it is what we must do to provide the best possible treatment.”

Soon We’ll Cure Diseases With a Cell, Not a Pill | Siddhartha Mukherjee | TED Talks

How are models used in medicine? How does a faulty or limited model negatively impact our approaches to treating the human body? Really great TED talk about these questions

Current medical treatment boils down to six words: Have disease, take pill, kill something. But physician Siddhartha Mukherjee points to a future of medicine that will transform the way we heal.

This is why you shouldn’t believe that exciting new medical study

“It’s a fact that all studies are biased and flawed in their own unique ways. The truth usually lies somewhere in a flurry of research on the same question. This means real insights don’t come by way of miraculous, one-off findings or divinely ordained eureka moments; they happen after a long, plodding process of vetting and repeating tests, and peer-to-peer discussion. The aim is to make sure findings are accurate and not the result of a quirk in one experiment or the biased crusade of a lone researcher.”