[mavu]

Can't possibly be secondary effects of Covid-19, right?

Because that would mean we would have to acknowledge long covid, and possible immune system damage of repeated infections.

That won't do at all, people might want not to die for the ecconomy.

[1over137]

>Can't possibly be secondary effects of Covid-19, right?

It could be because of that, which is why the article address it, and accounts for in in their modelling.

[goatlover]

You can't separate the economy from the well being of people.

[dmbche]

On a short timescale, when talking to coked up rich guys, I think people are able to separate the two.

[drekipus]

Good point

[anonporridge]

When robots do all the work you can.

[AnimalMuppet]

Even robots need energy. If the robots produce the stuff, the robots are "the economy", even if money isn't involved. And if the robots can't produce because of lack of energy, that still hurts people.

[neurobama]

The evidence-based research on long COVID is limited and of mixed quality. From what I've seen, there are significant gaps in experimental methodology and a lot of studies based on extremely subjective surveys which don't effectively filter co-morbities. I would strongly caution you to examine the data before adopting a strong position on the issue and applying it to potentially unrelated phenomena.

[deathanatos]

Long COVID, from what I can tell / have read, is acknowledged by research[1][2]. The mechanism of it, and what is the best treatment for the people suffering from it, I think, are still the subject of ongoing research.

[1]: https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/...

[2]: https://www.nature.com/articles/s41579-022-00846-2

(etc.; there are numerous studies that are not hard to find?)

[onos]

If the mechanism is not known, what do we know? Since almost everyone got covid, in what sense do we know this is at all associated with covid?

[neurobama]

If you look closely at what those studies actually survey and how that data is collected, it's clear that many possible co-morbid conditions aren't adequately filtered out. There is _no_ objective test or evidence-based protocol that can conclusively diagnose "long COVID." Doctors must rely on checklists of broad symptoms and the patient's word. The CDC page you linked mentions this under "Data for Long COVID:"

>For example, some studies look for the presence of Long COVID based on self-reported symptoms, while others collect symptoms and conditions recorded in medical records. Some studies focus only on people who have been hospitalized, while others include people who were not hospitalized.

Can serious health conditions can arise as a lasting consequence of infection? Absolutely. Can we easily distinguish between those and other symptoms or conditions which may arise out of stress, anxiety, other unrelated conditions, or symptoms which in some (not all) cases may be psychosomatic? Can we accurately draw conclusions as to how many long-COVID patients there are in total, then apply that conclusion in other areas with confidence as the GP commenter has done? Not yet.

[photon12]

Though it is worth noting there is experimental clinical data providing a plausible pathway to suggest long COVID is a result of cell activity dysfunction with plausible biomarkers of dysfunction, and we've known this long enough to know long COVID is a real medical pathology (one distinct from acute infection based on biomarker evidence):

Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism, https://molmed.biomedcentral.com/articles/10.1186/s10020-022...

Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function (Preprint), https://assets.researchsquare.com/files/rs-2448315/v1/8043bd...

[neurobama]

>A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%.

The first paper used a random forest-based decision tree classifier built on markers in blood assays. Neat.

However, this study has a major flaw. They rated their classifier's accuracy on classifying blood marker profiles of acutely ill COVID patients, long COVID patients experiencing "diffuse symptoms" referred with "no selection process", and a healthy control group. The control group consisted of healthy patients whose blood samples had been banked prior to the COVID-19 pandemic.

It's not clear whether they compared their classifier's results against people who've had COVID and recovered without issue, versus those who had COVID and continued to experience symptoms long after recovery. That is the entire point of developing such a classifier. This paper is worthless without that comparison.

Second paper has the same problem, and is honest about it:

>The healthy control subjects were individuals without disease, acute illness or prescription medications and were previously banked in the Translational Research Centre, London, ON (Directed by Dr. D.D. Fraser; https://translationalresearchcentre.com/). These latter samples were obtained prior to the emergence of SARS-CoV-2 in our region and therefore, were considered not to have been exposed to the virus.

[photon12]

These are fair criticisms. Fodder for future studies.

I only added these to say there's enough evidence to be questionable of the null hypothesis with regard to long COVID physiopathology.

[neurobama]

Absolutely, and I'm glad you linked them! Cool to read about applications of ML in healthcare.