patrick jane forums

On 13 May, David Crowe published a paper on the magical COVID-19 antibody tests. In short he concludes that there isn’t a shred of evidence that these tests actually correctly find COVID-19 antibodies.
These tests can be used at a later time to “prove” that the vaccines work (more antibodies in the vaccine group than in the placebo group)...

See some excerpts.

The major antibody types that are looked for are IgM, believed to be a generic infection fighting antibody that arises about a week or so after infection, and IgG, believed to be more specific, and believed by some to take longer for the body to create. After the infection is resolved, IgM antibodies are believed to gradually disappear, while IgG remain, providing ongoing immunity.
Unfortunately, this idealized picture is not supported by the available evidence, either because the evidence does not exist, is insufficient, or because it directly contradicts the model.
Positive antibody tests should be impossible before the person is first infected (RNA positive). Yet, old blood samples (2019 or before) have tested positive in significant numbers. Almost 14% of saved blood from old donations tested positive in a Dutch study, and in the validation of the Cellex and Chembio tests, 4.4% and 3.6% of old samples were positive.
(...)
Simple models that illustrate the timing of antibodies show the quantity (titer) rising smoothly and, for IgM, eventually peaking and declining smoothly. Yet many studies have found negative tests throughout the symptomatic period. A test developed by the Wadsworth Centre in New York found 40% of samples negative for antibodies 11-15 days after symptoms started, and even more between 16-20 days. This indicates that antibodies may come and go randomly and not behave in a smooth and predictable fashion.
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Other problems with antibody tests include a significant number of samples testing antibody positive from people who were COVID-19 RNA negative (although some had ‘COVID-like’ symptoms), with no evidence that the person was ever infected. In one Chinese study the positive rate on presumably never infected people was 25%.
(...)
But a far bigger problem is that the number produced is impossible to validate. When 1.5% of Santa Clara volunteers tested positive, it was assumed that that was truth. This ‘truth’ asserts that all of these people were RNA-positive at some point in the recent past. But there is absolutely no evidence for this. The ‘truth’ assumes that all the people were negative for COVID-19 antibodies prior to the assumed period of RNA-positivity. But there is absolutely no evidence for this.
(…)
The only jurisdiction with a formal structure for approval of antibody tests is the United States but, until very recently, it was just a charade, as the test manufacturers did not need to provide validation data. Now, validation data must be provided, but the FDA can only do a paper analysis [3].
(...)

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The Wadsworth test [11] simultaneously detects IgA, IgG and IgM antibodies, so cannot be used to distinguish the timing of different antibodies. However, it surprisingly had negative results on 40% of samples from people who were known to have been RNA positive for 11-15 days, 43% positive for 16-20 days, and 12% positive for more than 20 days.
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But there is no evidence that the fractions of the population that are antibody positive are meaningful, for several reasons:
• The presence of antibodies is taken to mean that the person was previously RNA positive with no symptoms, or minor symptoms. None of the surveys have proof that all the people, or even a majority, were previously RNApositive (and presumed infected), and the time has obviously passed to obtain this information.
• The people were assumed to be antibody negative prior to becoming RNA positive. None of the surveys have evidence for this.
• The absence of antibodies is taken to mean that the person was never COVID19 RNA positive. None of the surveys have evidence for this.
• It is assumed that the tests used would all give approximately the same result. Since there has been no cross-validation of tests, this is an unfounded assumption.

David Crowe - “Antibody Testing for COVID-19” (2020): http://theinfectiousmyth.com/coronavirus/AntibodyTestingForCOVID.pdf
(http://archive.is/hJTOP)



There is even a reference to the Dutch report from Sanguin, whose predecessor is the Red Cross that’s close to the European Royal degenerates, that got me interested in the corona antibody tests in the first place.
My conclusions about the Dutch tests were:
1) The experiment wasn’t defined prior to its start. This made it easy to manipulate the results because they could have changed the experiment as they went along.
2) They verified the test, with the test itself. If the test is severely flawed, they would never find out (this is arguably Crowe’s most important conclusion).
3) If 14% tested positive pre-pandemic? How come only 3% tested positive in the blood sample after the epidemic had been raging?

We additionally validated the ELISA using panels of plasma and serum samples from (i) Dutch blood donors collected in March and April 2018 (n=282; 1/282 seropositive), (ii) PCR-confirmed
COVID-19 patients admitted to Dutch intensive care units in March 2020 (n=10; 9/10 seropositive; 1/10 seronegative), (iii) Dutch PCR-confirmed COVID-19 patients with only mild symptoms (n=11; 11/11 seropositive), (iv) Dutch plasma donors with a documented PCR-positive test result who were at least 14 days fully recovered from mild or moderate COVID-19 symptoms (n=153; 151/153 seropositive), and (v) patients with common HCoV, CMV or EBV infection (n=40; 0/40 seropositive).
 (...)
In total 7,361 donations were tested from donors without known history of COVID-19, of which 230 were repeat reactive in the Wantai total antibody assay (3.1%). For 218/230 repeat reactive donors archived material of a previous donation was available for testing, showing seroconversion in 188/218 donors (86%) and pre-outbreak reactivity in 30/218 (14%); for 12 repeat reactive donors no pre-outbreak samples were available.

https://www.researchsquare.com/article/rs-25862/v1
(http://web.archive.org/web/20200502014538/https://www.researchsquare.com/article/rs-25862/v1)

Pay special attention to test results reported on only 10 and 11 (!) blood samples! If you know anything about statistics, you know that these results are worthless!
Nowhere in the paper does it say when the plan for this trial was defined. In other words they could have made it up as they went along to get the desired results.

One way to manipulate the results is by selectively choosing the regions displayed.
See the following that suggests that the highest rates were in Limburg (this should be Noord-Brabant more to the west), but would be different if the regions were chosen differently. These numbers aren’t given in detail in a table (it matters a lot on how many samples this is based).


It is admitted that no elderly people tested positive for antibodies but these results are obviously swept under the rug!
Blood donors in the Netherlands are up to 80 years, why did they didn’t they release test results on anybody over 72 years?!?