You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.

Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.

Some of my machine learning books.

Hands-On Machine Learning from @aureliengeron and Deep Learning with Python from @fchollet.

Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.

The importance of T-cell immunity!
A recent study has found that after one dose of Pfizer’s vaccine, patients had a strong T-cell response, and a boosted antibody response after their second dose. This is good news for many reasons so let’s read up.

This study found that people’s antibodies were moderately effective against the original strain after their first dose of Pfizer’s vaccine, less effective against the B.1.1.7 variant, and were unable to neutralize the B.1.351 variant. Read what I said, that’s only after ONE dose.

However, they had strong T-cell responses against ALL known variants after the first dose. So why is this important? To recap, T-cells stimulate B-cells to make antibodies. Antibodies are just your first line of defense which is what is initiated when you get a vaccine.

It’s our T-cells that are responsible for long-term immunity. When antibodies diminish after your initial inoculation, your T-cells-will tell your B-cells it’s time to produce more antibodies. As long as your T-cells still recognize this virus and inform your B-cells they need

to produce antibodies, the vaccine is still doing its job. Your antibodies will get built up over and over again. Just FYI your TCRs and your BCRs target different parts of the Coronavirus’ spike protein. Antibodies can prevent infection, your T cell-responses ensure that those

@br0oklynzo0 @KatyMontgomerie @ElPolloDiabloX @AbigailShrier @chadfelixg Okay, so you haven't read the study in the British Journal of Sports Medicine, you've read a Guardian story about it.

I have now read the study, and I teach critical appraisal as part of my job, so let's dive into it a bit.

@KatyMontgomerie @ElPolloDiabloX @AbigailShrier @chadfelixg First things first, the sample size in this study is tiny. 46 people. Plus, the point of comparison was "all women and men under the age of 30 in the Air Force between 2004 and 2014" rather than a longitudinal group.

@KatyMontgomerie @ElPolloDiabloX @AbigailShrier @chadfelixg So no matching for age, background, or physical condition. The result of these is that you're going to get a lot of statistical artefacts.

@KatyMontgomerie @ElPolloDiabloX @AbigailShrier @chadfelixg Second of all, there were three points of comparison here and you're ignoring two of them. The researchers measured for push-ups and sit-ups as well, both of which showed physical advantage removed entirely by hormones. Which means you're cherrypicking.

@KatyMontgomerie @ElPolloDiabloX @AbigailShrier @chadfelixg So what do you get when you have a tiny sample size and cherrypicked results? That's right, you get noise in the data. Take a look at the results graph from this paper you haven't read.

“Should I give them more fluid?”

Extremely common question during AKI referrals from junior docs.

⛔️ Fluid management is tricky, often misunderstood, & can cause huge iatrogenic harm ⛔️

We try to convey important principles in THREAD 👇

#tipsfornewdocs #medtwitter #tweetorial

Currently too often patients with high creatinine are given too much IV fluid, driven by an AKI ➡️ fluid reflex

We’ve seen 7 litres be given in one shift.

Medics & surgeons, juniors & seniors, it seems everyone loves to reach for extra IV fluid (especially if urine output poor)

Why does huge volume IV fluid ‘resuscitation’ consistently occur?

Lots of reasons, including FALSE beliefs:
🛑 IV fluid always “treats AKI”
🛑 Hypotension = hypovolaemia
🛑 Lactate >2 means fluid deficiency
🛑 Septic patients are very hypovolaemic

The rule with IV fluids and AKI is to remember this 5 word phrase:

💥 💥 Aim for euvolaemia, then stop💥 💥

Fluid status is like Goldilocks - hypovolaemia is bad for the kidneys BUT excess volume is also harmful for renal function and will cause other complications too.

It is easy to say “target euvolaemia” - but we agree it is more difficult to do!

Fluid status assessment is certainly made harder by a belief that we must only rely on a snapshot assessment of examination features which are NOT specific / sensitive / reliable to elicit like.…

New NHS reforms announced today. Seems to be a story of two parts, plus a missing character. Quick thoughts (1/):

Part 1 of the story is a set of technical fixes wanted by NHS leaders to encourage collaboration in the health system. The direction here makes sense—and fits with what the NHS has been doing for ages. Competition is (mostly) out; collaboration is in: good (2/)

And the need for legal changes to tidy the mess left by Lansley’s 2012 Act has long been recognized. But... (3/)

The benefits of integration are often way overstated, and the risk of reorganizations underplayed. In its first 30 years, the NHS’s structure was relatively stable. But over the past 30, the NHS in England has been on an almost constant treadmill of reform and reorganization (4/)

Overall, evidence suggests that previous reorganizations have delivered little measurable benefit. They can also distract and disrupt, depending on how they’re done. The list of other things for the NHS to be doing during and after the pandemic is very long. So be cautious (5/)

ICYMI, public health officials fear community transmission of B.1.1.7 (the "UK variant") is underway in Ontario.

I'll dissect each piece of worrying news from this story.

1. Simcoe Muskoka has so far reported 3 positive tests in individuals *with no known links* to the massive B.1.1.7 outbreak at Roberta Place LTC.

Two of those are linked to separate outbreaks of their own (a different LTC and a mental health hospital). Testing underway.

2. Of the 225 cases at Roberta Place, 100+ are confirmed or preliminary + for B.1.1.7.

42 household contacts of ppl linked to the outbreak are also COVID+. Public health unit is worried about keeping cases (which are all likely B.1.1.7) from rippling further into the community.

3. A case in Kingston area who tested positive for COVID "several weeks ago" was just discovered to be B.1.1.7. That person travelled to Simcoe Muskoka, but had not travelled internationally, according to the local health unit.

4. Overall, Public Health Ontario counts 47 B.1.1.7 cases as of yesterday -- these are only the ones confirmed thru full genome sequencing. Preliminary screening tests not included (yesterday's 99 in Simcoe Muskoka, for example).

Here's a breakdown by health unit courtesy PHO: