What if the false diagnosis rate was 90%?
Don’t misunderstand me. I’m a thorough believer in the existence of this virus.

@scottorandojin To exemplify: the NHS is under severe winter pressure. Many staff are unavailable, largely due to forced isolation from testing. Also, I’m told the adaptations to the conditions make normal work almost impossible. Trying to keep apart patients in different categories must be...
@scottorandojin ...incredibly stressful. I doubt I could ever have coped. Add to this the real stream of sick people & it’s a complete nightmare. My sympathies won’t be accepted but I offer them anyway. As to “the deaths”, do you mean the reported count of “covid19 deaths”? Everyone is doing...
@scottorandojin ...their very best. But as to that count: it’s nothing to do with clinical judgement. A covid19 death is a death from any cause within 28d of a positive test. So once a person is labelled by a positive test, if they then die, they’re a covid19 death. Back to my substantive...
@scottorandojin ...question: how reliable is that labelling? It’s not a clinical diagnosis. We’re not used to this idea. Never before in medical history has someone had a disease (let alone cause of death) been defined by the result of a single year alone. Yet that’s where we are. I think that..
@scottorandojin ...is absurd & kafkaesque. I recall Whitty saying many months ago that the true way to evaluate when a pandemic (epidemic, etc) comes to an end is when excess deaths reduce. I would add the caveat that we’ve chosen to restrict access to medical services for almost nine months...
@scottorandojin ...now. We’ve seen large falls in all sorts of referrals for cancer, heart disease and the like. I don’t think anyone expects that to do anything but to steadily push up all-causes mortality. Yet it will do so in a gradual way. It would not be apparent in the way respiratory...
@scottorandojin ...virus epidemics show themselves in the excess deaths record, which is a spike. Instead it’ll be a slow, inexorable rise. I fear that is what we’re largely seeing now. I say this because recently, the PHE weekly record of all-causes mortality definitely isn’t spiking, in the...
@scottorandojin ...way we’d expect if there were thousands of correctly diagnosed deaths from a contagious respiratory virus. Here are the last two weeks. Worth reading the short narratives.
@scottorandojin Surely, if the thousands of recent “covid19 deaths” were all correctly attributed, there must be quite a pulse of excess all-causes mortality?
I expect the ‘wait two weeks’ warning will be given & I do worry about that. Nothing in this mornings tweets asks anyone to do...
@scottorandojin ...anything except to think & to ask questions.

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@ukiswitheu I invite people to run the thought experiment: “what if the ‘cases’ data is inaccurate?”
Ignore ‘cases’, look instead only at excess deaths (per M Levitt’s tweet). Does that look characteristic of an epidemic? It’s completely diff from spring or any winter flu outbreak.
London:


Can anyone explain why there is no ‘2nd wave’ of excess deaths in London, without invoking herd immunity?
It’s not lockdown. See NW England:
This is the largest #SecondaryRipple (which I predicted).


https://t.co/b0rT5Lq9HI
Now check the 3 predictions I made months ago. They’ve all happened. Compare predictions from SAGE’s statements: they’re all wrong.
Even neutrals at this point might ask themselves “if he’s been right on all predictions, maybe he’s correct now?”


I’ve been saying since the Lighthouse Labs got up & running that I’m deeply sceptical about the trustworthiness of their ‘cases’ data. I showed how, at low virus prevalence, the PCR mass testing data was throwing out potentially 90% positives being

https://t.co/t4qQN4rH0u
I got ‘fact checked’ a LOT over that statement. This paper just published, about precisely that time period I speculated about. Turns out that high-80s% of Dr Healy’s positives by PCR were FALSE. This alone is sufficient in my view to throw severe doubt...
I urge all followers who have read my criticisms of PCR mass testing in U.K. to carefully read Mr Fordham’s carefully worded letter. Note that the innovation minister in the Lords, Lord Bethel, already admitted that the PCR system doesn’t have the equivalent of an MOT. https://t.co/zXzeDMKCBb


Without this information it’s impossible to interpret any result. If the oFPR is 4%, for example, and if the true prevalence is 0.3% (it’s probably less), then for every 10,000 tests, 400 positives would be false & 30 positives would be genuine. So 93% of positives are false.

As Mr Fordham points out, almost all policies pivot on PCR mass testing. Hancock previously admitted on talkRADIO to Julia Hartley-Brewer in late summer that the FPR was “just under 1%”. That was a flat lie (possibly inadvertent but he’s never corrected the record). The reason...

...we are sure Hancock told a lie is that they have never known the FPR. Those including Hancock who believe that the oFPR can be estimated by inspection of the lowest positivity ever recorded, while logical, is completely wrong. Changes in personnel, throughout, testing...

...architecture & the like can radically alter the oFPR. Since Hancock’s remark in late summer, PCR mass testing has moved into the Lighthouse Labs & this creates a new & urgent need to continually assess oFPR. I’ve good reason to believe it’s now VERY much higher now that the...

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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


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%)

👇

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