Category:
Medicine
One of the stranger medical problems a person could suffer from is "recurrent sudden death." In fact, one might think it impossible to suffer from such a problem. However, the term appears fairly often in medical literature. A few examples:
Atlas of Heart Diseases - Arrhythmias : electrophysiologic principles, 1996
New England Journal of Medicine - June 3, 1982
I think, though I'm not entirely sure, that "sudden death" is being used as a synonym for "cardiac arrest." Doctors are aware that the term "recurrent sudden death" sounds absurd.
Stedman's Medical Dictionary (2006) advises them not to use it:
And yet the term continues to appear.
A 1985 letter in the
New England Journal of Medicine reported the unusual case of a 70-year-old woman who kept hearing music playing in her head, particularly the song "When Irish Eyes Are Smiling." After ruling out other possible causes, her doctor eventually suspected the music might be due to the high doses of aspirin she was taking. And sure enough, when she reduced her aspirin intake, the music stopped.
I would never have thought that aspirin could cause musical hallucinations!
Tampa Bay Times - Apr 2, 1986
The letter itself
is behind a paywall, but I was able to find a brief article that the woman's doctor (James R. Allen) wrote about the case in the magazine of the Minnesota Medical Association.
Minnesota Medicine - Nov 2008
For housewives on the verge of a nervous breakdown.
Medical Economics - Mar 2, 1959
The Lancet reports on
the case of a 62-year-old German man who received 217 Covid vaccinations over a period of 29 months. That works out to getting vaccinated approximately every four days.
When I got the Covid vaccine I felt for a day like I'd been run over by a truck. The German hypervaccinator, on the other hand, felt no vaccine-related side effects.
Presumably the guy thought that all the vaccinations would give him super-immunity. When medical professionals realized what he had done, however, they were more worried that the opposite would happen — that he would build up "immune tolerance" and be more susceptible to Covid, not less. But when they checked him out, he seemed just fine.
More info:
arstechnica.com
Make your wife pleasant again with Premarin!
The physician who puts a woman on "Premarin" when she is suffering in the menopause usually makes her pleasant to live with once again. It is no easy thing for a man to take the stings and barbs of business life, then to come home to the turmoil of a woman "going through the change of life." If she is not on "Premarin," that is.
By the 1990s, Premarin had become the most frequently prescribed medication in the United States. Now,
according to Wikipedia, it's down to number 283.
The word 'Premarin' is a portmanteau of PREgnant MAre uRINe.
JAMA - Aug 16, 1958
In 1834, Dr. Peter Parker obtained a medical degree from Yale University and then traveled to China as a medical missionary. There he commissioned Chinese painter Lam Qua to make portraits of patients at the Canton Hospital who had large tumors. Yale now has 86 of these portraits in its collection.
Peter Parker seems to have been a fairly common name before it became permanently associated with Spider-Man.
More info:
Yale University Library
via
Design You Trust
I've seen this cautionary tale about putting too much faith in AI referred to in several places. It involves an AI program that had seemed to have "reached a level of accuracy comparable to human dermatologists at diagnosing malignant skin lesions."
Venturebeat.com tells the rest:
However, a closer examination of the model’s saliency methods revealed that the single most influential thing this model was looking for in a picture of someone’s skin was the presence of a ruler. Because medical images of cancerous lesions include a ruler for scale, the model learned to identify the presence of a ruler as a marker of malignancy, because that’s much easier than telling the difference between different kinds of lesions.
I tracked down the original source of this story to an Oct 2018 article in the
Journal of Investigative Dermatology:
"Automated Classification of Skin Lesions: From Pixels to Practice":
Dermatology images are the easiest to capture of all medical images, but also the least standardized. Standardization of images is difficult, even with dermoscopic images, as shown in Figure 1. Variability must be incorporated into training algorithms to create capacity to handle noisy data. This includes multiple camera angles, different orientations, blurry photos, multiple skin backgrounds, pen markings or rulers included in the photo, or variations in lighting. Otherwise, the algorithm will use features of nonstandardized photos to guide decision making. For instance, in our work, we noted that the algorithm appeared more likely to interpret images with rulers as malignant. Why? In our dataset, images with rulers were more likely to be malignant; thus the algorithm inadvertently “learned” that rulers are malignant. These biases in AI models are inherent unless specific attention is paid to address inputs with variability.