In the 1950s, doctors routinely pointed X-ray machines directly at the bellies of pregnant women.
▲ 1.3k r/nutritionally+1 crossposts

In the 1950s, doctors routinely pointed X-ray machines directly at the bellies of pregnant women.

In the 1950s, doctors routinely pointed X-ray machines directly at the bellies of pregnant women, using the technology to check a baby’s position or see if a mother was carrying twins.

It was considered an innocent, modern medical marvel. But it was giving those babies cancer.

When childhood leukemia cases began mysteriously surging across Britain in the mid-1950s, Dr. Alice Stewart suspected something that almost no one in medicine wanted to accept: the danger was starting before the children were even born.

When she applied for research funding, the Medical Research Council rejected her, declaring her theory wasn't worth investigating.

Armed with a modest £1,000 grant from a small charity, Stewart set out to find the answer herself. She tracked down the mothers of every British child who had died from cancer and sat down to interview them, listening carefully to details the medical system had dismissed.

The mothers remembered what the doctors had forgotten.

The data revealed a devastating pattern: children who died of leukemia had been X-rayed in the womb at twice the rate of those who survived. A single prenatal X-ray doubled a child's risk of developing fatal cancer.

When Stewart published her findings in 1956, the medical establishment didn't just question her work, they mocked it.

X-rays were the celebrated miracle of the postwar era. Doctors used them to treat acne, and shoe stores even placed open X-ray machines near their entrances so children could watch the bones in their feet move for fun.

The resistance grew even fiercer as the Cold War intensified. Governments and nuclear industries were investing billions into atomic energy, and Stewart’s research raised a terrifying question: if tiny doses of radiation could kill a child in the womb, what were "safe" radiation levels doing to everyone else?

Nuclear agencies, medical boards, and radiobiologists launched a campaign to dismantle her reputation. They claimed mothers' memories were unreliable, called her statistics flawed, and dismissed her as "hysterical."

Her university denied her a full professorship and eventually forced her into retirement at age sixty-eight.

Stewart simply moved to another institution and kept working.

It took more than twenty years of relentless pressure, but by the late 1970s, the medical world finally surrendered to her evidence. Routine X-rays on pregnant women were banned, protective lead shields became mandatory, and shoe-store scanners disappeared. Her refusal to back down saved hundreds of thousands of children worldwide.

Yet Stewart still wasn't finished.

In her seventies, she helped analyze health records from workers at the Hanford nuclear site in the United States, proving that low-dose radiation was causing cancer at levels ten times lower than official safety standards allowed.

When government officials revoked funding and locked her out of the records, Stewart fought through the courts for thirteen years until the data was released under the Freedom of Information Act.

In January 2000, the U.S. government officially acknowledged that radiation had killed weapons factory workers, leading to federal compensation for over 100,000 affected families.

Dr. Alice Stewart worked until she was ninety and passed away at ninety-five, having spent nearly half a century defending public health against immense institutional power.

We often assume that authority figures and established systems automatically know what is safe, leaving little room for ordinary people to question the status quo.

u/NichtFBI — 4 days ago

Dancing*, and Smiling* Israelis. Side note: you didn't need to use Face Recognition software.

u/NichtFBI — 16 days ago

They've evolved into three-factor. And I was already signed in earlier on my computer. But I guess a minor inconvenience is better than a massive inconvenience.

u/NichtFBI — 18 days ago

You know, I'm actually starting to like Pooh Bear. Him and Russia seem to be the only ones actually keeping the "bad guys" in check.

u/NichtFBI — 18 days ago

Why is it every time someone calls out / exposes someone from Israel, a hit piece is made of them?

u/NichtFBI — 18 days ago

The illusion of AI as authority or even reliable. An example using AI as an assistant. It can't really read.

While there are a lot of good cases for AI. It shouldn't be used as authority, as evidence, nor should it be used to proof.

In the first image, it shows that AI changed the transcription:

changed: [(IIII)] HIC DICIT MATER VESTRA CETTHEA
to this: [(IIII)] HIC DICIT ROMA ERIESTRA CETHEA

The purpose of using AI was to aid in my correction of my OCR text using the OpenAI API to indicate any minor issues such as scribal abbreviation errors, spacing errors, and letter damage.

I thought one of the use cases for AI was to assist in work like this. When there are 177,000 lines of text to correct and go through, it gets to be a bit much, and human error increases. Instead, using AI, if you don't test, compare, and manually look over it, you may be producing something that looks correct but is ultimately completely wrong.

I didn't want to rely on mathematical reasoning techniques alone as I had been. Such as, I used 23 different methods to extract each line from each column from each page, and trained 26 OCR models to read the text. In doing so, I manually corrected the first 25,000 lines of the text in order to specifically train the OCR. It allowed me to measure in real-time the amount of errors being introduced, rectified, among other things.

Why do the process over so many times? The reason for this can be found in images 2 and 3 of the gallery. Sometimes the algorithm cuts off lines, sometimes it adds two or more lines together, or it adds apart of another line; sometimes there are corrective notes in the margin or above a line; sometimes the line has a considerable amount of ink that has bled through, or maybe the line is just corrupted and smudged, and incomprehensible. Well each time I cut out new lines and OCR a new version, I eliminate error. But, I wanted all errors eliminated.

At first, I tested out by sending Luna, Terra, and Sol the first column of transcription along with the actual image of the column I was transcribing using reasoning models: none, medium, and high.

A major issue that became apparent was that they weren't reading it at all. They were adjusting text which looked like text it had seen while training, and writing that down instead.

In every single case, it changed:

>changed: ET DIVISIT LVCEM AC TENEBRAS
into this: ET DIVISIT LVCEM A TENEBRIS

The reason that is significant is because "A TENEBRIS" are from newer renditions (most popular; Clementine Vulgate, 1600CE) that are 1000 years younger than the line that says "AC TENEBRAS" (Codex Amiatinus, 700CE)

It became more evident that the higher the reasoning was, the more it tried to conform to the texts that are easily accessible online. If this were easily accessible, I would just use that. I have been through this process once before back a few years ago, and while reading it, I keep finding errors, and so I am doing this again to rectify that past edition.

And so, I tried sending all of the lines one by one. And still, it tried to conform it to some other text it has read before.

changed: VISIO THRONI D̅I̅ ET IMAGINIS EIVS SVPER THRONU̅
to this: VISIO THRONIDI ET IMAGINIS EIVS SVPER THRONI

And one of the most obvious cases where it was using prediction instead of so-called transcribing with vision or 'reading:'

changed: ENIM TE MAGIS INTERPRETVM VARIE
to this: ENIM TE MAGIS INTERPRETVM VARIETATE TVRBARI ET AMORE QVO LABERIS

Overall, the first 25,000 lines were reduced from a near 100% accuracy (I'm sure there is some human error somewhere in there) down to an 88% accuracy. And 32% of all 177,000 lines were altered. In a random test of 256 random lines from the ones changed; I manually corrected them, compared them to the original OCR, and AI versions. The original OCR had a less than 1% error-rate (mostly spacing and punctuation), while the AI corrected versions had a 15% error-rate. These are character by character error-rates.

u/NichtFBI — 1 month ago

Most can point to a favorite character before learning they share the same area within a spectrum of personality because the patterns they see are familiar to their internal self, i.e., their ego, e.g., we like people and root for those who share our cognitive patterns.

u/NichtFBI — 1 month ago