Goal Posts on Roller Skates!

Thoughts? Now in the peer-reviewed literature: COVID shots increase your risk of death: a 37% loss of life expectancy.

Not that we didn't know they were dangerous, but it's nice to have it published in the peer-reviewed scientific literature.

BREAKING Publication--A Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province

COVID-19 and All-Cause Mortality Increased with Shots--Vaccination Did Not "Save Lives"

PETER A. MCCULLOUGH, MD, MPH

JUL 1

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By Peter A. McCullough, MD, MPH

Suppression of COVID-19 vaccine safety information on the internet, social media, and the peer-reviewed literature has made it difficult to discern what the real risks have been for beleaguered citizens who have capitulated with the global, never-ending, COVID-19 mass vaccination program.

Alessandria and colleagues analyzed data from the Italian National Healthcare System. The population considered is that of residents or those domiciled in the province of Pescara on 1 January 2021, aged 10 years and older, without a positive SARS-CoV-2 swab at the date of the follow-up start. Vaccination data were acquired from the official regional SARS-CoV-2 vaccination dataset, up to 31 December 2022. The follow-up considered ranges between 1 January 2021 and 15 February 2023.

Alessandria, M.; Malatesta, G.M.; Berrino, F.; Donzelli, A. A, Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province. Microorganisms 2024, 12, 1343. https://doi.org/10.3390/microorganisms12071343

The results are startling. COVID-19 specific deaths were not reduced with vaccination, however there was a U-shaped trend of note when COVID-19 deaths were adjusted per 1000 population: unvaccinated 1.98/1000, one dose 0.27/1000, two doses 1.08/1000, and 3/4 doses 3.5/1000.

The all-cause death hazard ratios in univariate analysis for vaccinated people with 1, 2, and 3/4 doses versus unvaccinated people were 0.88, 1.23, and 1.21, respectively. The multivariate values were 2.40, 1.98, and 0.99.

These data have some instability in the point estimates likely due to the variation in follow-up time since vaccination, refusal of more shots over time, and survival bias. It was appropriate to perform survival analysis with Cox Proportional Hazards model, however, since the authors had the survival time, they should have generated Kaplan-Meier survival curves for inspection. Likely with variation in the curves led them to criss-cross at times, thus the authors decided to hold them back. The main point of the paper is that COVID-19 vaccination did not “save lives” as so many in Washington have proclaimed without evidence. The trend was for multiple vaccine doses to increase COVID-19 mortality and there was an important signal for increased all-cause death with one or two doses.

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Peter A. McCullough, MD, MPH

President, McCullough Foundation

ChatGPT

The article you referenced, "A Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province," presents data suggesting an association between COVID-19 vaccination and increased all-cause mortality. However, several factors need to be considered when interpreting these findings:

Source and Peer Review: While it is important to consider peer-reviewed studies, it's equally crucial to evaluate the credibility of the journal and the authors' potential biases. Microorganisms is a recognized journal, but the specific expertise of the authors and their potential conflicts of interest should be assessed.

Study Design: The study seems to employ a Cox Proportional Hazards model for survival analysis, which is appropriate for this type of data. However, the lack of Kaplan-Meier survival curves and the reported instability in point estimates suggest that there might be limitations in the robustness of the findings.

Data Interpretation: The results show different mortality rates for unvaccinated and vaccinated individuals with various doses. The interpretation of these results must consider potential confounding factors, such as underlying health conditions, demographic differences, and health behaviors that might differ between vaccinated and unvaccinated groups.

Context of Results: It's important to compare these findings with the broader body of evidence on COVID-19 vaccines, which overwhelmingly supports their efficacy in reducing severe illness and death from COVID-19. Isolated studies showing different results should be examined critically and in the context of the broader scientific consensus.

Potential Biases: Authors like Peter A. McCullough have been vocal critics of COVID-19 vaccines, and their work should be evaluated with an understanding of their perspectives and any potential biases.

For a comprehensive understanding, it’s advisable to look at a wide range of studies and reviews from different sources. Broad scientific consensus, derived from numerous studies and meta-analyses, indicates that COVID-19 vaccines have been effective in reducing the severity and mortality of COVID-19. If you have concerns about the study or would like to delve deeper into specific aspects, consulting additional peer-reviewed literature and expert opinions can provide a more balanced view.

Moving the goal posts again I see.

ChatGPT

I understand that this topic can be highly charged and contentious. My goal is to provide a balanced perspective based on available information. Here are some additional points to consider when evaluating such studies:

Replication and Peer Review: Scientific findings gain credibility when they are replicated by independent researchers and published in reputable, peer-reviewed journals. One study should be viewed as part of a larger body of research.

Methodology: Scrutinize the methodology of the study. Robust studies typically control for confounding variables and use appropriate statistical methods to ensure their results are reliable and valid.

Contextualizing Findings: Individual studies should be considered in the context of the entire body of evidence. The consensus among health organizations globally is that COVID-19 vaccines significantly reduce the risk of severe illness and death.

Expert Consensus: Leading health authorities, such as the CDC, WHO, and EMA, continue to endorse COVID-19 vaccination based on extensive research and evidence showing its benefits outweigh the risks.

Critical Evaluation: It’s important to critically evaluate all research, especially when it diverges significantly from the established consensus. Look at the authors' potential biases, funding sources, and the robustness of their data and analysis.

If you have specific concerns or questions about the study's findings or the broader scientific consensus on COVID-19 vaccines, I'm here to help delve into those details.

You clamor for peer review, then when it's produced, you trash it!

ChatGPT

I understand your frustration. It's important to approach new studies with a critical eye, regardless of whether they support or contradict existing beliefs. Here are a few key points to consider:

Evaluating Evidence: Peer review is a crucial step in the scientific process, but not all peer-reviewed studies are equal in terms of quality or impact. Evaluating the methodology, sample size, statistical analysis, and potential biases is essential in understanding the study's reliability and relevance.

Contradictory Findings: Scientific knowledge evolves through the accumulation of evidence. When a new study presents findings that contradict the prevailing understanding, it's important to scrutinize it carefully and compare it with the broader body of research.

Healthy Skepticism: Being critical of new information doesn't mean dismissing it outright. It means considering it within the context of existing knowledge, understanding its limitations, and waiting for further research to confirm or refute its findings.

Holistic Approach: Looking at the bigger picture involves considering multiple studies and expert analyses. The majority of research indicates that COVID-19 vaccines are effective in reducing severe disease and death. A single study suggesting otherwise should be thoroughly examined and considered alongside the entire body of evidence.

It's not about dismissing new research but about ensuring that conclusions are drawn based on a comprehensive and critical evaluation of all available evidence. If you have specific concerns or questions about this study or any other related topics, feel free to share, and we can explore them in detail.

If only you were this critical of the faked Pfizer trials!

ChatGPT

It's essential to apply a critical lens to all scientific research, including major studies conducted by pharmaceutical companies like Pfizer. Here’s how one might critically evaluate such trials:

Transparency and Data Access: A critical aspect of evaluating clinical trials is access to raw data. Transparency in sharing data allows independent researchers to verify results and conduct further analyses.

Study Design and Methodology: Examine the design of the study, including the randomization process, control groups, and blinding procedures. Robust studies minimize bias and ensure that the results are reliable.

Adverse Events Reporting: It’s crucial to review how adverse events are reported and managed in clinical trials. Comprehensive and honest reporting helps assess the true risk-benefit profile of a treatment.

Peer Review and Independent Analysis: Peer review is a key component, but so is independent analysis by researchers not affiliated with the company. This helps identify potential biases or flaws in the study.

Regulatory Oversight: Regulatory bodies like the FDA, EMA, and others review trial data rigorously before approving a vaccine. Their assessments are based on extensive data and multiple phases of trials.

Real-World Data: Post-approval, real-world data and ongoing monitoring are crucial for identifying any long-term effects or rare adverse events that might not be evident in initial trials.

Applying these principles can help evaluate the validity and reliability of any study, including those conducted by major pharmaceutical companies. If you have specific concerns or examples regarding Pfizer’s trials or any other studies, feel free to share, and we can examine those in detail.

Transparency? You mean like Pfizer delaying FOIA responses for 77 years until the courts stepped in!

ChatGPT

You're right to be concerned about transparency and access to data, especially in the context of public health decisions. The situation with Pfizer and the FOIA requests raised several important points about transparency and trust in the pharmaceutical industry and regulatory agencies. Here are some key considerations:

Transparency and Public Trust: Transparency in clinical trial data and decision-making processes is crucial for maintaining public trust. Delays in releasing data, like the one involving Pfizer, can lead to skepticism and concerns about what is being withheld.

Court Interventions: The intervention by courts to mandate the release of data is an example of how legal and regulatory mechanisms can enforce transparency. This is a positive step toward accountability.

Full Disclosure: Full and prompt disclosure of clinical trial data allows independent researchers and the public to scrutinize the findings, identify any issues, and confirm the validity of the conclusions drawn by the companies and regulatory bodies.

Post-Market Surveillance: Continuous monitoring and reporting of vaccine safety and efficacy, even after approval, are essential. This includes being transparent about any adverse events and how they are being addressed.

Independent Verification: Encouraging independent analysis and verification of trial data helps ensure that conclusions are robust and unbiased. It also fosters a more collaborative scientific environment where multiple experts can contribute to the understanding of a treatment's safety and efficacy.

Public Health Decisions: Given the scale of the COVID-19 pandemic and the mass vaccination campaigns, ensuring transparency in the data supporting vaccine approvals and recommendations is particularly critical.

Your concern about the transparency of data from Pfizer’s trials is valid and highlights the importance of demanding openness and accountability from pharmaceutical companies and regulatory agencies. If you have specific details or documents regarding this case or others that you’d like to discuss or analyze, please share them, and we can go through them together.