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 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through it!

https://t.co/jGt006Vlh5
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇
Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇
Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇
Let's now test the group of 999,900 healthy individuals:

▫️ 989,901 of them will be diagnosed (correctly) as healthy (99%)

▫️ 9,999 of them will be diagnosed (incorrectly) as sick (1%)

👇
Since your test came back positive, it means that you belong to either one of the groups that had a positive result:

1. 99 people that are truly sick, or
2. 9,999 people that are actually healthy (but were diagnosed as sick.)

👇
Basically, out of 10,098, only 99 are truly sick.

That'll give you a 0.98% chance of being sick!

So no, most likely, you are fine!

👇
Here is something important: this is true as long as our only priors are that 1 in 10,000 people have the disease.

For example, if you were showing symptoms, then your chance of being sick after receiving a positive test will be higher.

More from Santiago

More from Health

1/
Remember woman who tuk multiple @SriSriTattva products 4 range of problems frm diabetes 2 gas 2 liver disease & developed liver failure, listed for liver transplant?
Here is original thread:
https://t.co/PXxI1Slyv2
23 samples, Analysis results
#MedTwitter #livertwitter


2/
Before I go into results, I must say this was overwhelming. There was SO MUCH the lab identified, impossible to put everything here. So I made a summary. At the end of this thread, I have linked a full analysis described in Excel format. Some results were VERY concerning

3/
How did we analyse?
Here R links 2 methods
They R high end, done under strict protocols
Frm Ministry of Forest, Environment, Climate / NABL approvd Lab
ICP-OES https://t.co/O1CLhqVQAu
GC MSMS https://t.co/zRJoXyWQIr
FTIR https://t.co/goAembQ08p
Here is list V analysed 👇


4/
Sample names written on top (each column).
First 5 samples: C what we identified in #Ayurveda #medicines
Antibiotics
Steroids (anabolic/synthetic)
#NARCOTICS - LSD, Morphine
Blood thinners (possible reason Y bleeding tests were off the roof in the patient)
Heavy metals!


5/
Next 5 samples (total 10 now)
Mercury is clear winner. Almost all samples
See controlled substances - Butyrolactones https://t.co/CPz0FwPEOm, methylamine https://t.co/OZnXY7U9UQ
Alcohols, industrial solvents
Rare metals - cobalt, lithium
Again lots of blood thinners
#Ayush

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