Yes, they’ve made it very clear that that’s the reasoning, and I am saying I disagree.
A) I still think they are not correct (long evidence below) B) Ct values are clearly somewhat useful, and the question is how much—and I do not think the public health comms apparatus should stifle somewhat-useful medical information reaching patients or doctors just because I might be misled. That’s just way too paternalistic.
As to why I think they’re wrong, I’ll cross-post from my fb thread against the specific pdf linked in op, though all other arguments seem isomorphic afaict. If you don’t trust my reasoning but want the reasoning of medical professionals, skip to the bottom.
Basically, the pdf just highlights a bunch of ways that Ct values aren’t perfectly precise and reliable. It says nothing about the relative size of the error bars and the signal, and whether the error bars can drown it out—and, they can’t. To use a very exaggerated metaphor, it’s like the people saying we need to pull J&J because it’s not “perfectly safe” without at all looking at the relative cost/benefit.
So, they give a laundry list of factors that will produce variability in Ct values for different measurements of the same sample. But toward the end of the the doc, they proclaim that these sources of variability change the result by up to 2-3 logs, as if this is a damning argument against reporting them. The scale of Ct values is 10 logs. Hospitalized patients vary by 5 logs. That’s so much more signal than their claimed noise! So their one real critique falls very flat.
However, they do understate the noise significantly, so we can strengthen their argument. Within-patient variability is already like 2-3 logs, as you can see from data here for example: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151491/. So variability of viral loads across different patients, different collection methods, and different analysis methods will have more like 4-6 logs of variation. That’s the stronger argument.
But even this is ultimately too weak.
Most of the variation is on the negative side: there are lots more ways to fail to get a good sample than there are to accidentally find the virus is more concentrated than in reality. So, low Ct values indicating high viral load are still very good signals! I don’t know the exact numbers here because they won’t report them many places, but your reasoning would hypothetically go: If you get a Ct value of under 20, you better start canceling meetings and preparing for a possible hospital visit. If you get a Ct value of 38, maybe it’ll end up getting much worse, or maybe not. Not much information there. This is simple reasoning—doctors do it all the time with other tests with high falsity rates, saying “if you test positive on this you probably have X, but getting a negative doesn’t rule it out.”
And aside from this asymmetry, just the correlation is also really useful! I am not the first person to say this: googling turns up a bunch of instances of medical professionals saying similar things:
Yes, they’ve made it very clear that that’s the reasoning, and I am saying I disagree.
A) I still think they are not correct (long evidence below)
B) Ct values are clearly somewhat useful, and the question is how much—and I do not think the public health comms apparatus should stifle somewhat-useful medical information reaching patients or doctors just because I might be misled. That’s just way too paternalistic.
As to why I think they’re wrong, I’ll cross-post from my fb thread against the specific pdf linked in op, though all other arguments seem isomorphic afaict. If you don’t trust my reasoning but want the reasoning of medical professionals, skip to the bottom.