Additionally, a lot of those numbers are based on earlier strains of COVID, which were much more severe.

I suspect the 1/5 figure is largely true for "has some degree of cardiovascular damage and worsened general health after COVID", but the number of people actually disabled by the condition is much lower.

That said, any loss of ability is a sad thing, and I am incredibly disappointed that we did not introduce any shared indoor space air quality legislation post-pandemic.

There are probably a lot of syndromes caused by latent infections from virii like EBV, various herpes, HPV, etc that don't go properly diagnosed or treated if they can be treated at all.

Also, it's been found that some viral infections retreat to tissues where their detection might be difficult/impossible without biopsy. You saw this with, for example, Ebola, where survivors could test negative for the virus, but it would still survive and replicate in different isolated tissues like those in the eye.

Never enough to warrant going to a doctor unless I was being super paranoid (and spend a long time convincing them I wasn't paranoid) but just enough to always wonder if there was something more to the story.

Perhaps its a reflection of how hard it is to get the medical community to take Long COVID seriously ?

I would say there is more chance of them (sadly) telling you to go home, take a couple of paracetamol and get some rest. Or if you're lucky, they might mis-diagnose you with something else....

(No first-hand experience here, just going by one or two anecdotal stories I've heard on the grapevine)

For a variety of reasons, hyping the threat of infection has been a pretty widespread practice among the medical and scientific community since COVID began. There's no way on earth 1 out of 5 kids are still experiencing symptoms 3 months out.

I don't see how you'd know the exact number without a solid diagnostic check.

> Long COVID (LC) impacts one in five children after an acute SARS-CoV-2 infection

So, 20% of children that suffered of an acute SARS-CoV-2 infection, did not recovered immediately, but experienced also Long COVID.

Maybe not all infections are considered "acute".

Maybe you are not the type of person that people feel comfortable to share their health problems information with.

Anyway, because of the stratification many people don’t know anyone with long covid, while at the same time half of my friends have it. Both can be true observations of our surroundings.

On the actual numbers I would say that ~10% get some level of Long Covid while half of those recover in the first year. Of those who don’t recover around half have GJH which is a massive over representation suggesting a strong predisposition.

> We evaluated the diagnostic power (...). We estimated a 94% accuracy for (our method), significantly higher than the (traditional method) (66% accuracy).

Both methods have counting in their name, but they are comparing the diagnostic power.

Likely because you're attempting to spread some form of anti-vax nonsense.

They are looking into that.

Even if they did, the hormonal effects would likely swamp anything else. Which is a huge problem: women are routinely excluded from studies to avoid that, meaning we have no idea what the effects are on women.

> We evaluated the diagnostic power of the device in a cohort of 45 LC patients and 14 healthy pediatric donors. We estimated a 94% accuracy for the microclot count using the devices, significantly higher than the traditional counting of microclots on slides (66% accuracy).

They are comparing the predictive power and using accuracy (instead of sensitivity, recall, F1, etc.). For their method "using the devices", they compute an accuracy of the predictive power, not of the count, of 94%. For the previous method they say the accuracy is 66%.

Basic questions: Is accuracy even a good metric for this? Is 94% a good value or just the difference between bad and very bad?

It might very well be that their improvement is from bad to really good, but the point is that a raw stat of "94% accuracy" is useless without context and so is the headline.

https://pmc.ncbi.nlm.nih.gov/articles/PMC4614595/#:~:text=Ac...

That is exactly why I gave the trivial example of an "always No" test. It has perfect specificity (zero false positives) and has accuracy corresponding to prevalence. The sensitivity is zero, however, which is the point.

The primary conclusion of this research was basically just "this looks like it would be worth doing more research on." Which is a fair conclusion for a study this small.

> Approximately 6 in every 100 people who have COVID-19 develop post COVID-19 condition

vs., for example:

> From one center in Wuhan, 1,359 survivors completed 3-year follow up and 54% had at least one persistent symptom of Long Covid

This only underscores the lack of clinical definition. Both of these suffer from the same fundamental error, which, again, is that there's no precise definition of the syndrome. They include symptoms that are common amongst healthy people, mix them with less-common things that are associated with Covid (e.g. anosmia) and try to call this a disease state. See the WHO's grab-bag list of possible inclusion criteria:

> Over 200 different symptoms have been reported by people with post COVID-19 condition. Common symptoms include: fatigue, aches and pains in muscles or joints, feeling breathless, headaches, difficulty in thinking or concentrating, alterations in taste.

So literally having "headaches" or "aches and pains" is enough to claim Long Covid, according to the WHO.

The Topol/Aly substack engages in the same logic, and you will see that the referenced charts and graphs cover everything from fatigue to heart attack. Aly, in particular, has based his entire long covid research on a single dataset of (largely elderly, unhealthy prior to infection) VA patients that he refuses to release, and routinely engages in statistical fishing expeditions for new "symptoms" within that dataset.