Recent research has highlighted significant variability in the safety profiles of different batches of COVID-19 mRNA vaccines, particularly those produced by Pfizer. A study conducted by Jablonowski and Hooker identified specific vaccine lots with alarming rates of adverse events. Lots were noted for having high outliers in terms of death, serious adverse events, and all serious adverse events.
Further analysis from the “How Bad is My Batch” database reveals that 96% of the top 100 batches with the most crude deaths started with the letter “E.” Additionally, 55% of batches ranked by lethality, defined as the percentage of adverse events that were fatal, also began with “E.” This pattern indicates a potential correlation between batch identifiers and adverse outcomes.
The variability in batch safety is defended by arguments like that the distribution and handling procedures inherent in mRNA vaccine production was too complex at times, meaning that differences in manufacturing processes, storage conditions, and transportation could have led to the inconsistent vaccine efficacy and safety profiles. This batch-to-batch variability, shown by the differing rates of adverse reactions observed among vaccine recipients. is a critical factor. It is very orderly done. Too orderly.
How Bad is My Batch search reveals: Of the top 100 batches with the most crude deaths, 96% started with “E.”
Based on lethality (% of adverse events that were fatal), 55% started with “E.” A study by Jablonowski and Hooker found: “Five lots had high outliers for death (i.e., EL0140, EL9261, EL3248, EN9581, and EJ1686); four for serious (EK4176, EK5730, EH9899, and EJ1685), and five for ALL SAEs (EK5730, EH9899, EK4176, EK9231, and EJ1685). These vaccinations were the first to be distributed in December 2020 and early 2021.”
McCullough Foundation: Some people received ‘dud’ COVID-19 injection batches associated with minimal adverse events. Others were given dangerous batches linked to a serious injury and death. You can check how bad your batch was here: https://knollfrank.github.io/HowBadIsMyBatch/HowBadIsMyBatch.html
How bad is my batch
The research paper by Jablonowski and Hooker, titled Batch-dependent safety of the BNT162b2 mRNA COVID-19 vaccine in the United States was published in the journal Science, Public Health Policy and the Law.
The research focuses on the batch-dependent suspected adverse events (SAEs) following vaccination with Pfizer’s BioNTech COVID-19 mRNA vaccine. Their study provides critical insights into the variability of adverse outcomes across different vaccine lots. The study builds on earlier findings from Denmark by Schmeling, Manniche, and Hansen, which identified unexpected batch-dependent SAEs following vaccination, and vaccine lot data obtained through a Freedom of Information Act (FOIA) request by the Informed Consent Action Network (ICAN) in October 2022, tracking vaccine batches from manufacturing to administration sites.
Jablonowski and Hooker identified High-Outlier Lots; specific Pfizer vaccine lots with significantly higher rates of adverse events particularily distributed in December 2020 and early 2021. They also found clear Batch Characteristics and Adverse Events; larger vaccine lot sizes were associated with lower SAE rates in Denmark, a trend that Jablonowski and Hooker compared to U.S. data.
Further analysis using the “How Bad is My Batch” database revealed that 96% of the top 100 batches with the most crude deaths started with the letter “E.” Additionally, 55% of batches ranked by lethality (the percentage of adverse events that were fatal) also began with “E.” This indicates a potential correlation between batch identifiers and adverse outcomes. The lots include:
- EL0140, EL9261, EL3248, EN9581, and EJ1686 for high outliers in death.
- EK4176, EK5730, EH9899, and EJ1685 for high outliers in serious adverse events.
- EK5730, EH9899, EK4176, EK9231, and EJ1685 for high outliers in all SAEs.
Earlier batches sent to mass distribution centers like hospitals had more side effects compared to later ones sent to pharmacies and large grocery chains. This suggests that distribution and handling procedures may have influenced batch performance. The study highlights the extreme lot-to-lot variability in the Pfizer BioNTech vaccine, illustrating potential problems associated with the manufacture of these products.
The scientific method
Mixing batches within a population is a strategic approach in scientific evaluation that enhances the robustness and reliability of comparing different batches of a product, such as a drug or vaccine. This method leverages the diversity of batch exposure across a population to provide a broader dataset for analysis, which can reveal subtle differences in efficacy, safety, and other critical attributes. It aids the scientific evaluation by having an increased sample size and variability, ensuring a sufficient number of observations for statistical analysis. Larger sample sizes distributed in a wider variety of people reduce the impact of random variation, making it easier to identify batches with atypical performance, minimizes influence of external variables, make multivariate analysis. By observing the outcomes in a mixed population, researchers can use statistical methods like cluster analysis or principal component analysis to group individuals based on their batch exposure and compare the groups’ outcomes, which can reveal batch-specific effects, such as higher adverse event rates or differential efficacy, which might be obscured in a more homogeneous population. Mixing batches within a population allows researchers to compare the performance of each batch under real-world conditions. This can be done through longitudinal studies or post-market surveillance, where data on adverse events and efficacy are collected over time. And of course; mixing batches in a population in real world provides scientific insights directly translatable to public health and clinical practice, particularily so for vaccine production.
Neat, right?
Implications
The “How Bad is My Batch” initiative’s analyzed data from a mixed population clearly identify batches with higher adverse event rates. Only, there is an unnerving catch. The statistical curves of the batches are exceedingly well-planned, showing clear trends in adverse event rates over time and across batches, and the consistency and predictability of these patterns indicate an extremely well-planned and deliberate design rather than random variation. Specific batches were intentionally varied to test different formulations or concentrations within the population.
Another marker is the controlled distribution pattern; that earlier batches sent to mass distribution centers like hospitals had more side effects compared to later batches sent to pharmacies and grocery chains. This indicates certain batches were prioritized for specific settings. This also aligns with a systematic testing strategy and implies that the vaccine industry may have been evaluating the impact of different batches under varying conditions to assess the injections in real-world scenarios. Well-planned statistical curves typically result from controlled experiments where variables are manipulated systematically. The precision of these real-world curves suggests that the vaccine batches were not randomly produced but were part of a structured testing protocol aimed at observing differential outcomes.
Regulatory guidelines from the FDA, EMA, and WHO emphasize quality control and batch release testing, repeatedly claim the batches of Covid-19 mRNA vaccines were and are consistent – yet the observed variability proves the giant failure of the regulatory systems. And if adverse event rates are consistently linked to specific manufacturing changes or batch identifiers, it logically follows that these changes were intentional. The observed patterns proves a systematic approach to testing the effects of different production variables in these injections on a large scale, using the public as a testing ground, to gather comprehensive safety data under real-world conditions, with the method of mixing batches enhancing the detection of batch-specific effects. Albeit, perhaps, with a tiny bit of ethical implications.
Alphabetic labeling of toxic batches, arranged alpha-numerically.
So, looking at it from this perspective, it is difficult not to think that the large-scale experiement was about how to most efficiently cause different sorts of fast and slow-working damage in different populations, all in accordance with the concept of bio-weapons.
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