#TeamClots preprinted an important milestone in the microclot theory of Long Covid etiology. 🧵on the implications, as I understood them. TLDR: it looks like microclot burden will struggle as a standalone biomarker for LC; we should look deeper!
First, why is this an important milestone? This paper uses imaging flow cytometry to quantify the microclot burden in patients, which, AFAIK, is the first time that microclots have been measured in an objective and statistically robust way.
The flow cytometry method captured the following measurements: number of microclot objects per milliliter, mean microclot area, microclot count in area range.
These measurements quantify the microclot burden in terms of microclot number and size. There was a distinction made between microclot objects and microclots that I didn't understand, but what I say applies to the measurements they report in Table 2.
Long Covid patients (#pwLC) had more microclots and microclots of greater size *on average*. But, healthy controls (HCs) had a significant microclot burden as well: 25% of HCs had more microclot objects than 50% of #pwLC; 25% of HCs had larger microclots than 50% of #pwLC.
This suggests that microclot burden (as measured in the paper) will struggle as a standalone biomarker for LC. It seems unlikely that microclot burden correlates with symptomatic burden, and it's not clear why microclot presence is enough to indicate anticoagulation.
The results don't tell us much about whether platelet activation markers can serve as standalone biomarkers. There's also the question of whether microclot composition distinguishes Long Covid, and the largest microclots may be clinically meaningful.
As one of the most rigorous quantifications of microclots, this is an important milestone. My takeaway is that microclot size / number may be part of the story, but, after this, it’s unlikely to be the full story. Thanks #TeamClots, looking forward to the follow-ups!
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What if the next medical breakthrough is hidden in plain text? Causal estimates drives progress but data is limited & RCTs slow. Introducing NATURAL: a pipeline for causal estimation from text data in hours, not years.
Paper:
Site: tinyurl.com/ppr29 tinyurl.com/web98
NATURAL in a nutshell: take an expert-designed study protocol → filter for social media posts that conform to the protocol → LLMs extract distributions over key variables → adapt classical techniques (e.g., IPW) for treatment effect estimates.
We developed six observational datasets to evaluate this pipeline, paired with corresponding ground truth from randomized trials: two synthetic datasets constructed using marketing data, and four clinical datasets curated from public migraine and diabetes forums.
I decided to write out a part of the story of my illness for my #LongCovidMoonshot holiday letter, which I am sending tomorrow to @RepRaulGrijalva, @SenMarkKelly, and @SenatorSinema. 🌼
To be fully transparent here, I consider potatoes to be the second-most inferior starch (yuca takes the crown), and I think this is an objective assessment.
The criteria that I use is, how enjoyable is it to eat without being smothered in fat and salt?
Potato shills should feel free to unfollow or block. 👋
I've heard privately from a few non-patients, who interact regularly with Long Covid patients on Twitter, that we can be unwelcoming, and, at times, vile, in our advocacy. I want to lay out some scattered thoughts on how I have come to think about my own advocacy, such as it is.
First, while I believe advocacy should never be vile, I acknowledge that there are different approaches, different strategies. This how is I think about it.
Also, I'd like to acknowledge that anger and rage are very valid emotions. It's also deeply unfair that the marginalized and disabled are being called upon to be tempered and gracious, when the actions and choices of those with power and ability have and continue to hurt us.