Why this doc thinks the Lancet report is fake
I agree with the Doctor, whose Tweet you posted, who rightfully thinks the Lancet report is entirely fictional and an intentional effort to confuse and misinform. It is part of the ubiquitous propaganda being officially pumped into the media for the express purpose of enhancing the worldwide effects of SARS-COV-19.
My experience and training told me this pathogen was a product of deliberate design from the moment I first studied its genome. I knew months ago this pathogen would duplicate characteristics of HIV, and no vaccine could ever be developed for it, as it is just not possible. I knew this pathogen would rapidly mutate, and have the ability to quickly scramble a segment of its RNA signature, giving the impression of an explosion of different strains. But, the rapid mutations of this pathogen are NOT, caused by natural environmental effects, this ability is deliberately part of its design. It is specifically meant to make normal efforts to develop vaccines meaningless.
What good is a biological agent, if a simple vaccine can be quickly developed to defeat it by an enemy? If the “enemy” is the human population of this planet, it would be best if a pathogen could perform its mission without hindrance.
I think the mission is population control, which has been the mission for many years now. Why else would there be so much calculated propaganda and efforts to promote meaningless vaccines, instead of effective treatments?
Nice find nyhetersverige.
I am not an IT guy by any means, (though I was a computer science major for a year), but have a small experience of being a user of complex hospital IT systems. (Lab, pathology, radiology, pharmacy and prescription writing systems are all separate and their integration is not always smooth.)
This blogger points out a number of problems with the data collection, the lack of compatibility of various databases, barriers to sharing between database systems, missing information, the inevitable loss of information as researchers do “propensity matching.”
This gives more of a breakdown of the data and uses something called “propensity matching”. This is a method of matching up similar patients.
So for instance you want to get the same proportions of black/white patients in each group. Same for BMI, smoking, etc
Each time you do this for each factor you reduce the pool of patients to compare. So to get the same number of black, non-smoking, BMI 25, hypertensive people in each group is really tough (5 factors)
This group did it with 23 factors, all matched up “perfectly”
Whoa!! That is quite impressive! I am NOT a statistician, but this strikes me as profoundly improbable. Here is the table of the 23 factors:
This is pretty much statistically impossible, and they are claiming that they got this matching in 7000 patients out of a pool of 96000 patients for which they received high quality information from 671 hospitals.
Nope. Sorry, the data is too “clean”.
They point out that PCR information on each patient is pathology, and lab data is not imported to the hospital patient record, but is linked to separate pathology databases. This also makes it harder to believe that AUTOMATED data sharing systems were in place BEFORE the pandemic started from such a mix of international hospitals and labs (including Senegal, Turkey, Pakistan and of course, china). I agree that this is fishy.
Great analysis. When trust in authoritative institutions is waning and smart people look closely at things, it gets harder to pull the wool.
Very good point! If the decision is binary, part of a selection or not, using 23 factors would lead to more than 8 million possible partitions (2^23). When you only have 90 thousand samples, you run into problems really really fast. Typically machine learning algorithms are used for this type of analysis. I do not know what kind of method they used, nor am I interested. This fact in itself is sufficient to reject this research.
And Chris picked up on it in his latest YouTube report. There are multiple, independent reasons to believe that the data is faked. The data is impossibly homogeneous across countries….. and across so many metrics, as pointed out by the math competent DaveDD.
Subject change but tangentially related:
I have a question for you that I don’t think has been brought up here before. Is it at all possible that the deaths due to vaping, that occurred about a year to a few months prior to the covid outbreak could have been Covid?
There was a lot of confusion and mystery early on with this mysterious illness and the lung symptoms seem to be similar, to a layman anyway. Is it possible it was misdiagnosed?
Hi agitating prop,
Good question. What is missing in this possibility is infectivity. If it was covid, many more elderly would have died due to the same symptoms as they are dying of now. So it is highly unlikely that it was covid.
Where did the data come from? Surgisphere allegedly knows, but They Aren’t Talking.
And, it seems, they screwed up some numbers on Australia. This led to Questions.
The study, led by the Brigham and Women’s Hospital Center for Advanced Heart Disease in Boston, examined patients in hospitals around the world, including in Australia. It said researchers gained access to data from five hospitals recording 600 Australian Covid-19 patients and 73 Australian deaths as of 21 April.
But data from Johns Hopkins University shows only 67 deaths from Covid-19 had been recorded in Australia by 21 April. The number did not rise to 73 until 23 April. The data relied upon by researchers to draw their conclusions in the Lancet is not readily available in Australian clinical databases, leading many to ask where it came from.
In a statement Surgisphere said it stood by the integrity of its data, saying all information from hospitals “is transferred in a deidentified manner” but could not be made public.
Yes, we saw that last night. Quite extraordinary.
> In a statement Surgisphere said it stood by the integrity of its data, saying all information from hospitals ‘is transferred in a deidentified manner’ but could not be made public.
Riiight. I have a frog in my pocket but I can’t let you see it.