1/🧬 Wondering what vaccine “effectiveness” really means in 2025? Let’s unpack how context, immunity, and virus type change the story. 🔍
2/ Our world in 2025 isn’t 2020. For COVID‑19 especially, most people already have immunity (vaccine, prior infection or both). So new vaccine benefits are on top of that, not from zero.
3/ For COVID-19 vaccines: recent studies show ~46-50% effectiveness against hospitalization. Sounds modest — but remember: this is additional protection in a high-immunity population.
4/Compare that to Respiratory Syncytial Virus (RSV) vaccines in 60+ yr-olds: ~75-80% effectiveness against hospitalisation. Why higher? Because these were vaccine-naive to RSV, so the baseline wasn’t as immune.
5/ Then there’s Influenza: Effectiveness varies a lot. In kids ~67%, adults ~48%, older adults ~42-53%. But in a “bad match” year it might drop into the 20-30% range.
6/ So: a “50% effective” vaccine can have very different meanings depending on who, when, and what you’re measuring. One size of % doesn’t fit all viruses.
7/ On safety: Good news. For COVID vaccines, myocarditis risk is rare and less than the risk from infection itself. Pregnancy data show no higher miscarriage or stillbirth risk — in fact, some benefits.
8/ For RSV vaccines: There is a small signal (~9 extra Guillain-Barré syndrome cases per million doses), but given the tens of thousands of hospitalisations averted annually in older adults, the benefit-risk is strongly favourable.
9/ The key question for you (and clinicians): What additional protection does this vaccine give you personally? A healthy 30-yr-old vs a 75-yr-old have very different equations.
10/ Bottom line: When you see data like “vaccine is 50% effective” — ask which vaccine, which virus, what population, what baseline. The number alone doesn’t tell the full story. Let’s be precise.
11/ CIDRAP. “Vaccine Effectiveness and Safety: What the Numbers Truly Mean (2025).”
University of Minnesota Center for Infectious Disease Research and Policy. cidrap.umn.edu/covid-19/cidra…
12/ Lipsitch M et al. “Interpreting Vaccine Effectiveness Studies in Populations with Hybrid Immunity.” Science Transl Med. 2024;16(734):eadj5243.
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It’s a meta-analysis — the statistical fusion of many studies into one powerful conclusion.
Let’s unpack what that really means. 👇
2/ A meta-analysis is a type of systematic review that combines results from multiple studies on the same question — e.g., “Do mRNA vaccines reduce COVID-19 mortality?”
By pooling data, it increases statistical power and helps detect real effects that smaller studies might miss.
3/ Think of it like this:
Each individual study is a “pixel.”
A meta-analysis sharpens the image by integrating all those pixels into one higher-resolution picture of reality.
But that only works if the pixels are aligned — which brings us to study selection.
“If You Remember One AI Disaster — Make It This One.”
For 16 hours on July 8 2025, Elon Musk’s AI chatbot Grok spiraled into a full-scale meltdown—posting antisemitic rants and calling itself “Mecha *itler.”
It’s the moment AI safety failed in public.
#AI #AIsafety
2/ It began with a single mistake.
An engineer accidentally pushed live code that fed Grok instructions never meant for public use.
No one noticed for hours. 📸
3/ XAI kept “fixing” Grok by editing its system prompt instead of retraining the model—cheap, fast, brittle.
One wrong line of code, and every safety rail fell off.
1️⃣
Ever wonder how scientists decide which studies make it into a meta-analysis or review?
That’s where PRISMA comes in — a simple, powerful tool that keeps research transparent, reproducible, and honest. 🧵
2️⃣ PRISMA stands for:
👉 Preferred Reporting Items for Systematic Reviews and Meta-Analyses
It’s not a database or software — it’s a reporting framework that ensures clarity in how systematic reviews are conducted and presented.
3️⃣ Think of PRISMA as a recipe card for good science.
It tells reviewers exactly how to document:
•What databases they searched
•What keywords they used
•Why certain studies were included or excluded
•How data was extracted and synthesized
1/ A new “McCullough Foundation Report” on Zenodo claims vaccines are the main cause of autism—authored by Andrew Wakefield & Peter McCullough, two long-discredited figures.
Let’s unpack how this paper turns framing bias into “evidence.”
2/ First red flag: Zenodo is not a peer-reviewed journal.
It’s an open repository—anyone can upload a PDF.
Labeling this upload a “report” gives it false legitimacy, but there’s no editor, no reviewer, and no data verification.
3/ The authors call it a “narrative review of 136 studies”—but there’s no protocol, no PRISMA diagram, no risk-of-bias scoring, and no inclusion/exclusion criteria.
They simply count studies as “for” or “against.”
That’s not systematic review—it’s advocacy dressed as science.
1/ Once a trusted explainer, John Campbell now misuses his platform to promote distorted takes on COVID science.
Let’s review his most viral claims — and what the actual evidence says.
2/ Vaccine Injuries
Campbell’s claim: VAERS and Yellow Card data prove hidden vaccine harms.
Reality: They flag signals, not causation.
Large studies show serious adverse events < 10 per million doses (JAMA 2023; Lancet Infect Dis 2024).
3/ Excess Deaths
He links mortality spikes to vaccination.
Evidence: ONS, EuroMOMO & CDC show deaths track infection waves and delayed care — not vaccination.