jamanetwork.com/journals/jam...
This is a great explanation of emerging causal discovery techniques applied to biology, by the always readable @ajadmon.bsky.social and Erin Carlton
Posts by Andrew Admon
Abstract text: Consideration of Sociodemographics in Machine Learning-Driven Sepsis Risk Prediction OBJECTIVES: Use of machine learning (ML) and artificial intelligence (AI) in prediction of sepsis and related outcomes is growing. Guidelines call for explicit reporting of study data demographics and stratified performance analyses to assess potential sociodemographic bias. We assessed reporting of sociodemographic data and other considerations, such as use of stratified analyses or use of so-called “fairness metrics", among AI and ML models in sepsis. DATA SOURCES: PubMed identified systematic and narrative reviews from which studies were extracted using PubMed and Google Scholar. STUDY SELECTION: Studies were extracted from selected review articles published between January 1, 2023, and June 30, 2024, and related to sepsis, riskprediction, and ML; we extracted studies predicting sepsis, sepsis-related outcomes, or sepsis treatment in adult populations. DATA EXTRACTION: Data were extracted by two reviewers using predefined forms, and included study type, outcome of interest, setting, dataset used, reporting of sample sociodemographics, inclusion of sociodemographics as predictors, stratification by sociodemographics or assessment of fairness metrics, and reporting a lack of sociodemographic considerations as a limitation. DATA SYNTHESIS: Thirteen of 96 review studies (14%) met inclusion criteria: 6 systematic reviews and 7 narrative reviews. 120 of 170 studies (71%) extracted from these review articles were included in our review. 99 of 120 studies (83%) reported a measure of geography or where data was collected. Eighty (67%) reported sex/gender, 24 (20%) reported race/ethnicity, and 4 (3%) reported other sociodemographics. Only three stratified performance results (2%) by sociodemographics; none reported formal fairness metrics. Beyond a lack of geographic heterogeneity (39/120, 33%), few studies reported a lack of sociodemographic consideration as a limitation
#NotEvenTrying
New in @sccmcriticalcare.bsky.social Crit Care Med, @hauschildt.bsky.social + @ajadmon.bsky.social review 120 ML or AI studies predicting sepsis, sepsis-related outcomes or sepsis treatment in adult populations
NONE reported formal fairness metrics
pubmed.ncbi.nlm.nih.gov/40488579/
Happy to see our perspective on postmarket surveillance of clinical models out in NEJM!! ai.nejm.org/doi/full/10....
I’m disappointed that I never got a set of these when I left the house!
#PCCMSky Update
We are now 2 starter packs deep!
Repost so the noobs know who the cool kids are
CCM only folks definitely welcome
Reply if you are not on one and should be (need to see your creds in bio)
go.bsky.app/2gcvXuy
go.bsky.app/88wg6oG
1/ Great advice for med students & residents on the DON’Ts for a personal statement:
tinyurl.com/yjewsknc
❌ Don’t write >1 page
❌ Don’t write before you think
❌ Don’t write about a momentous clinical experience
❌ Don’t focus only on medical experiences
❌ Don’t explain the specialty
I’ll preorder the coffee table book
Maggie made a checklist for her birthday party (with some spelling help :-)). This is going to be an epic fifth birthday bash!
You know what’s the real disappointment here? We never went all the way. We never had a logit vs probit showdown.
I tried to add to your total but was already following you!
Do not confuse "interesting thoughts about art" for ironclad instructions about how to make art.
Wait until we tell them about the benefits of a third stent
I think they turned it into a movie later, Regression Runner
That’s also why I like target trial emulation- it helps identify study design decisions that would have been clearly weird or impossible in an RCT (“we’ll enroll people today and start them on treatment sometime in the past six months!”) but sometimes happen anyways in observational studies
But also this :-)
Love it, but more importantly strong choice of colors.
I'd add a line between Asthma and death too, but the measured relationship is being distorted by the open path via the "Pneumonia and hospitalized" since they're conditioning on it
Conditioning on hospitalization for pneumonia induces a false (inverse) association between asthma and other severe risk factors/complications that get you admitted with pneumonia and makes it look like pneumonia patients do well.
Their inclusion criteria included both pneumonia and hospitalization, so that's the 'selection node' or 'collider' (the box at the top). Asthma is one reason for hospitalization with pneumonia (e.g., it's a risk factor for poorer outcomes, so the ED might admit). Septic shock is another.
Oooh, I’m going to use that with my 4 year old- “I need to see more efficacy data before I keep doing that”
Orange chicken 😢
📣 The AJRCCM invites you to submit your cutting-edge research in pulmonary and critical care medicine
@atscommunity.bsky.social #medsky
SUBMIT HERE: tinyurl.com/3hezurez
last night, Hallie Prescott had her NEJM review on sepsis released: www.nejm.org/doi/full/10....
Today I had the pleasure of giving some remarks on her installation as the Toews Family Legacy Professor at the University of Michigan
Figures with manually added references…
Hello, Bluesky! We've finally started building out our starter pack for our ATS member community. If you're a current or former member, let us know and we will add you. go.bsky.app/GLzJtqT #medsky #lungsky #pedsky
8-year-old Beau.
He’s a good boy and loves his little brother.
We’d never let you anyways
Who’s got it better than us?!