A sleeping brain may flag dementia years before memory does
Machine learning applied to routine EEG recordings is finding a signal in sleep that clinics were not looking for.
Bobjgalindo / Wikimedia Commons (CC BY-SA 4.0)
Machine learning applied to routine EEG recordings is finding a signal in sleep that clinics were not looking for.
The number that made this a story is not the largest one in the dataset. It is the one that moved fastest.
Every system has a quiet assumption holding it up. This one assumed redundancy that existed on paper but had been consolidated away over a decade of efficiency drives. Nobody decided to remove it; it simply stopped being funded.
How it started
The pattern held across every place we checked, which is usually a sign that the cause is structural rather than local. Where it broke down, it broke down for reasons that were specific, documented and — in retrospect — predictable.
“Nobody was wrong individually. The system was wrong collectively, which is much harder to fix.”
What changed on the ground
People adapted the way people always do: informally, quickly, and without a budget line. Staff rewrote their own protocols, called colleagues in other districts, and kept a shared spreadsheet that outperformed the official dashboard for six weeks.
That improvisation worked, which is both the good news and the problem. A system that survives on goodwill is not resilient; it is borrowing against people who cannot keep lending.
What happens next
Two reviews are open and one timeline has been published. The measures under discussion are unremarkable — buffers, second sources, mandatory notice periods — which is usually a sign that the fix is known and the will is the variable.
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