You can bet your last dollar and your house that none of these folks above, nor any other high profile personalities, really took the novel mRNA Covid-19 vaccine. They were all given saline solutions. The reason is very simple. Should any of these publicity stunts go awry with a Suspected Adverse Event (SAE) and heaven forbids, a death, the whole vaccination drive collapses. The risk to Big Pharma, not the actors, is just too big.
In November 2021, the head nurse of the University Medical Center in Ljubljana, Slovenia, resigned from her job and made a public announcement that based on her personal work routines, she claimed the novel Covid-19 vaccines came in 3 different markings. The ones marked "1" are saline solutions used as placebo for VIPs which accounted for 30% of the vaccines. Everyone else gets either those marked "2" or "3". Number 2 is classical RNA. The number 3 is an RNA stick that contains the oncogene associated with the adenovirus, which contributes to the development of cancer. Her short video clip is here.
The video caused a scandalous uproar in Slovenia. As it turned out, the 'chief nurse' was a Slovenian anti-vaxxer activist named.Vera Kanalec. The chief nurse of the University of Ljubljana Medical Center is Zdenka Mrak who has not resigned from her job. My point for showing this video is to highlight what the fake nurse said in November 2021 :
* 30% of vaccines are placebos
* Vaccines are identified by 3 different markers
* Vaccines are identified by 3 different markers
The initial novel vaccine narrative was "it will prevent a vaccinated person from catching Covid-19 and prevent the spread of the disease". When this was proven false, the narrative evolved to "it will prevent a vaccinated person from developing serious effects when infected". It is incredible a gullible world could not see through the new narrative for what is obviously an unfalsifiable fallacy.
The Danish study:
Due to Emergency Use Authorization, rapid production of Covid-19 novel vaccine and implementation of large-scale vaccination programs, there has been many reports of contamination and inconsistencies in toxicity levels and ingredients. There is also lack of reports on clinical data on individual vaccine batch levels. Consequently, there is no assurance of homogeneity of vaccine efficacy across all batches. If indeed this were so, then good luck to you as risk of SAE depends on which batch the dose that was injected into your deltoid came from.
A trio of researchers Max Schmeling, Vibeke Manniche and Peter Riis Hansen did a study on whether safety is batch-dependent using the Danish experience. Their peer-reviewed report was published by European Journal of Clinical Investigation on 30 Mar 2023. (Here).
The Danish Data :-
Period covered - 27 December 2020 to 11 January 2022)
Population - 5.8m
Numbers vaccinated - 4,026,575
Vaccine - BNT162b2 (Pfizer)
Number of batches - 52 (2340 to 814,320 doses per batch)
Doses administered - 10,793,766 doses
Total SAEs (Suspected Adverse Events) - 66,587 (some had no batch labels)
Total SAEs batch identified - 61,847
Total severe SAEs - 14,509 (23.5%)
SAE-related deaths - 579 (0.9%)
SAEs are classified into severity levels of (A) non-serious, (B) serious (hospitalization or prolongation of existing hospitalization, life-threatening illness, permanent disability or congenital malformation), and (C) SAE-related death.
For each batch, SAEs per 1,000 doses is computed.
The 2 variables of the 52 batches tracked are (a) SAEs per 1,000 and (b) number of doses per batch.
The unanalysed chart (each dot represents a single vaccine batch) :
Some statistics gibberish here. (Doesn't matter if you are not familiar with these):
Since the observed relationship between the numbers of SAEs and BNT162b2 vaccine doses was highly heterogeneous, conventional regression statistics were not considered to be applicable. Therefore, heterogeneity in the relationship between the numbers of SAEs and doses per vaccine batch was assessed by log-transformation followed by non-hierarchical cluster analysis and general linear model (GLM) test for differences in SAE rates between batches. Reporting of the study conforms to broad EQUATOR guidelines.
The analysed chart (each dot represents a single vaccine batch) :
Three regression or trend lines become apparent:
Blue line - the batches have high SAEs per 1,000 doses (means higher toxicity) and low number of doses per batch.
Green line - the batches have lower SAEs per 1,000 doses (means lower toxicity) and high number of doses per batch.
Yellow - the batches have no SAEs per 1,000 doses.
Danish vaccination reporting system provides anonymized data. Researchers can access individual information without medical privacy issues. Each batch data point can be further analysed to SAE severity levels of (i) total SAEs, (ii) serious and (iii) SAE-related deaths.
Interpretation:
The blue batches make up only a small 4.22% of total doses, but account for 70% of all SAEs (27% of those vaccinated with serious SAEs and 47% deaths, were caused by blue batches.)
The green batches make up the majority with 64% of total doses, but account for only 29% of all SAEs (72% of those vaccinated with serious SAEs and 52% deaths, were caused by green batches.)
The yellow batches make up 32% of total doses, but account for almost zero SAEs.
What can be inferred ?
1. There is no doubt whatsoever the Yellow batches are placebos.
2. The Blue batches have the highest toxicity but lower doses in each batch. Small but pack a lot of punch. This is intended to do the worse targeted damage. The fact blue batches make up only slightly less than 5% of total doses is highly suspicious. In statistical analysis, 5% is generally used as the threshold for significance testing. Anything higher draws attention and deeper scrutiny. For example, if someone has a winning formula for a casino game, if he attempts to win every game, he will be discovered fairly quickly by security. In WWII, when Britain broke into Nazi Germany's Enigma code which enabled it to locate enemy submarines, they only targeted 5% of the U-boats. Had Britain attacked all enemy submarines, then the Germans would have realised their Enigma code has been broken.
3. The percentages seem to fall into common categories of 5%, 30%, 50%, 60%, 70%. This does not seem accidental, but human connivance.
Calculate your risks:
Unless you are the privileged few to take placebos, and you are the smarter ones to want to know what is the risks now that data is available, let's review how you play with your life.
(a) Your overall chance of getting a Suspected Adverse Event is 1 out of 174, of getting a serious SAE is 1 out of 744, and of death is 1 out of 18,642.
(b) You have technically 32% chance of getting a placebo, a 64% chance of getting a toxic dose (green batch) and a tough luck 4% chance of getting a highly toxic dose (blue batch).
(c) If you are given a placebo, there will be no SAE. The SAE and death numbers are probably due to statistical anomalies from small population numbers.
(d) If you get the toxic dose, you have 1 out of 385 chance of getting SAE, a very very high 50% chance your SAE is serious, with a high 1 out of 59 chance of death.
(e) If your luck ran out and you got the highly toxic dose, you have a very high 1 out of 10 chance of getting an SAE. In which case, you have 1 out of 11 chance the SAE is serious and 1 out of 160 chance of death.
If the Danish data is universal, would you have taken the jab had you better understood the odds? Is 1 out of 18,642 chance of dying from a jab fine with you? Mind you, the Danish experience data is interesting for the fact their population base is fairly similar with Singapore's.
Vaccine - BNT162b2 (Pfizer)
Number of batches - 52 (2340 to 814,320 doses per batch)
Doses administered - 10,793,766 doses
Total SAEs (Suspected Adverse Events) - 66,587 (some had no batch labels)
Total SAEs batch identified - 61,847
Total severe SAEs - 14,509 (23.5%)
SAE-related deaths - 579 (0.9%)
SAEs are classified into severity levels of (A) non-serious, (B) serious (hospitalization or prolongation of existing hospitalization, life-threatening illness, permanent disability or congenital malformation), and (C) SAE-related death.
For each batch, SAEs per 1,000 doses is computed.
The 2 variables of the 52 batches tracked are (a) SAEs per 1,000 and (b) number of doses per batch.
The unanalysed chart (each dot represents a single vaccine batch) :
Some statistics gibberish here. (Doesn't matter if you are not familiar with these):
Since the observed relationship between the numbers of SAEs and BNT162b2 vaccine doses was highly heterogeneous, conventional regression statistics were not considered to be applicable. Therefore, heterogeneity in the relationship between the numbers of SAEs and doses per vaccine batch was assessed by log-transformation followed by non-hierarchical cluster analysis and general linear model (GLM) test for differences in SAE rates between batches. Reporting of the study conforms to broad EQUATOR guidelines.
The analysed chart (each dot represents a single vaccine batch) :
Blue line - the batches have high SAEs per 1,000 doses (means higher toxicity) and low number of doses per batch.
Green line - the batches have lower SAEs per 1,000 doses (means lower toxicity) and high number of doses per batch.
Yellow - the batches have no SAEs per 1,000 doses.
Danish vaccination reporting system provides anonymized data. Researchers can access individual information without medical privacy issues. Each batch data point can be further analysed to SAE severity levels of (i) total SAEs, (ii) serious and (iii) SAE-related deaths.
The blue batches make up only a small 4.22% of total doses, but account for 70% of all SAEs (27% of those vaccinated with serious SAEs and 47% deaths, were caused by blue batches.)
The green batches make up the majority with 64% of total doses, but account for only 29% of all SAEs (72% of those vaccinated with serious SAEs and 52% deaths, were caused by green batches.)
The yellow batches make up 32% of total doses, but account for almost zero SAEs.
What can be inferred ?
1. There is no doubt whatsoever the Yellow batches are placebos.
2. The Blue batches have the highest toxicity but lower doses in each batch. Small but pack a lot of punch. This is intended to do the worse targeted damage. The fact blue batches make up only slightly less than 5% of total doses is highly suspicious. In statistical analysis, 5% is generally used as the threshold for significance testing. Anything higher draws attention and deeper scrutiny. For example, if someone has a winning formula for a casino game, if he attempts to win every game, he will be discovered fairly quickly by security. In WWII, when Britain broke into Nazi Germany's Enigma code which enabled it to locate enemy submarines, they only targeted 5% of the U-boats. Had Britain attacked all enemy submarines, then the Germans would have realised their Enigma code has been broken.
3. The percentages seem to fall into common categories of 5%, 30%, 50%, 60%, 70%. This does not seem accidental, but human connivance.
Calculate your risks:
Unless you are the privileged few to take placebos, and you are the smarter ones to want to know what is the risks now that data is available, let's review how you play with your life.
(b) You have technically 32% chance of getting a placebo, a 64% chance of getting a toxic dose (green batch) and a tough luck 4% chance of getting a highly toxic dose (blue batch).
(c) If you are given a placebo, there will be no SAE. The SAE and death numbers are probably due to statistical anomalies from small population numbers.
(d) If you get the toxic dose, you have 1 out of 385 chance of getting SAE, a very very high 50% chance your SAE is serious, with a high 1 out of 59 chance of death.
(e) If your luck ran out and you got the highly toxic dose, you have a very high 1 out of 10 chance of getting an SAE. In which case, you have 1 out of 11 chance the SAE is serious and 1 out of 160 chance of death.
If the Danish data is universal, would you have taken the jab had you better understood the odds? Is 1 out of 18,642 chance of dying from a jab fine with you? Mind you, the Danish experience data is interesting for the fact their population base is fairly similar with Singapore's.
Given a low probability of catching Covid if one takes all precautions, and an extremely low fatality rate of less than 1%, would you have taken the novel vaccine in light of what this Danish study shows? Let's not even take into consideration long term unknowns.
Had the government known about this risk profile and not advised the public, it is malfeasance, and in the case of death, it is culpable homicide.
The inevitable question:
Now how in the world can that fake nurse know in November 2011 that 30% of the vaccines were placebos. If this was a coincidence, how did she know there are 3 classifications of vaccines based on the level of toxicity?
Conclusion:
The Danish study proves there is no batch homogeneity. The percentages seem to betray human connivance. The statistics seem to indicate intention to cause harm. The risk of serious SAEs including deaths with the the blue and green batches, are unexceptionally high, but averaged out overall presents a more acceptable risk level. But with no batch homogeneity, the Swedish study shows the safety of the BNT162b2 mRNA Covid-19 vaccine is batch-dependent. This is a critical piece of information that has not been presented to the medical community.
The Danish data carries a caveat of biased reporting. Just as the VAERS are notoriously under-reported, so too the data in this study, especially pertaining to vaccine deaths. The risk figures from this study could potentially be worse than shown here.
The US has the VAERS which has online information available for downloading. In Denmark, vaccine batches are registered by the Danish Serum Institute and all SAE cases with corresponding vaccine batch labels are reported to the Danish Medical Agency (DKMA) and classified by the DKMA according to SAE seriousness. The DKMA-managed spontaneous SAE reporting system accepts reports of SAEs from any source, for example healthcare providers, patients and other members of the public. Anonymised data are available for downloading by the public.
Singapore obviously has some similar reporting systems in place. But Singapore data is not available to the public.
Note:
My bad. I had been mentioning Swedish report when it should have been Danish. now edited. (11 Dec 2023).
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