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Opened a support case with vendor, with THEIR log file showing THEIR connection returning a Cloudflare 520 error - full HTML including a RayID they could check with dev tools.

Support response?

"Could you try that again, we don't have any reports of issues.”

#notEvenTrying #itsNotMe

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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

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/

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Traveling to Queretaro and the ground crew hilariously says "Have a nice trip to.......somewhere in Mexico!" #noteventrying

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Climate change Minister blames Greens for agreeing unreal... Ireland set to miss greenhouse gas targets as emissions r...

#Shame - #ireland one of only 2 EU states to miss greenhouse gas targets http://bit.ly/2ovOhfZ @harrymcgee #climate #NotEvenTrying

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Seriously, the fact that another two colleagues could be on their way out the door is not deliberate on my part... #NotEvenTrying

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