208 research outputs found
Use of metal-filling in spent fuel canisters as an alternative waste form
http://www.worldcat.org/oclc/2294914
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
In this work we introduce Labrador, a pre-trained Transformer model for
laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million
lab test results from electronic health records (EHRs) and evaluated on various
downstream outcome prediction tasks. Both models demonstrate mastery of the
pre-training task but neither consistently outperform XGBoost on downstream
supervised tasks. Our ablation studies reveal that transfer learning shows
limited effectiveness for BERT and achieves marginal success with Labrador. We
explore the reasons for the failure of transfer learning and suggest that the
data generating process underlying each patient cannot be characterized
sufficiently using labs alone, among other factors. We encourage future work to
focus on joint modeling of multiple EHR data categories and to include
tree-based baselines in their evaluations.Comment: 27 pages, 8 figure
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
As large language models continue to be widely developed, robust uncertainty
quantification techniques will become crucial for their safe deployment in
high-stakes scenarios. In this work, we explore how conformal prediction can be
used to provide uncertainty quantification in language models for the specific
task of multiple-choice question-answering. We find that the uncertainty
estimates from conformal prediction are tightly correlated with prediction
accuracy. This observation can be useful for downstream applications such as
selective classification and filtering out low-quality predictions. We also
investigate the exchangeability assumption required by conformal prediction to
out-of-subject questions, which may be a more realistic scenario for many
practical applications. Our work contributes towards more trustworthy and
reliable usage of large language models in safety-critical situations, where
robust guarantees of error rate are required.Comment: 10 page
Reply to Nepomuceno et al.: A renewed call for detailed social and demographic COVID-19 data from all countries
Demographic science aids in understanding the spread and fatality rates of COVID-19
Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs
Use of Dried Capillary Blood Sampling for Islet Autoantibody Screening in Relatives:A Feasibility Study
Background: Islet autoantibody testing provides the basis for assessment of risk of progression to type 1 diabetes. We set out to determine the feasibility and acceptability of dried capillary blood spot–based screening to identify islet autoantibody–positive relatives potentially eligible for inclusion in prevention trials. Materials and Methods: Dried blood spot (DBS) and venous samples were collected from 229 relatives participating in the TrialNet Pathway to Prevention Study. Both samples were tested for glutamic acid decarboxylase, islet antigen 2, and zinc transporter 8 autoantibodies, and venous samples were additionally tested for insulin autoantibodies and islet cell antibodies. We defined multiple autoantibody positive as two or more autoantibodies in venous serum and DBS screen positive if one or more autoantibodies were detected. Participant questionnaires compared the sample collection methods. Results: Of 44 relatives who were multiple autoantibody positive in venous samples, 42 (95.5%) were DBS screen positive, and DBS accurately detected 145 of 147 autoantibody-negative relatives (98.6%). Capillary blood sampling was perceived as more painful than venous blood draw, but 60% of participants would prefer initial screening using home fingerstick with clinic visits only required if autoantibodies were found. Conclusions: Capillary blood sampling could facilitate screening for type 1 diabetes prevention studies.</p
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IN-SITU MONITORING OF CORROSION DURING A LABORATORY SIMULATION OF OXALIC ACID CHEMICAL CLEANING
The Savannah River Site (SRS) will disperse or dissolve precipitated metal oxides as part of radioactive waste tank closure operations. Previously SRS used oxalic acid to accomplish this task. To better understand the conditions of oxalic acid cleaning of the carbon steel waste tanks, laboratory simulations of the process were conducted to determine the corrosion rate of carbon steel and the generation of gases such as hydrogen and carbon dioxide. Open circuit potential measurements, linear polarization measurements, and coupon immersion tests were performed in-situ to determine the corrosion behavior of carbon steel during the demonstration. Vapor samples were analyzed continuously to determine the constituents of the phase. The combined results from these measurements indicated that in aerated environments, such as the tank, that the corrosion rates are manageable for short contact times and will facilitate prediction and control of the hydrogen generation rate during operations
Fall in C-peptide during first 2 years from diagnosis: Evidence of at least two distinct phases from composite type 1 diabetes trialnet data.
Interpretation of clinical trials to alter the decline in β-cell function after diagnosis of type 1 diabetes depends on a robust understanding of the natural history of disease. Combining data from the Type 1 Diabetes TrialNet studies, we describe the natural history of β-cell function from shortly after diagnosis through 2 years post study randomization, assess the degree of variability between patients, and investigate factors that may be related to C-peptide preservation or loss. We found that 93% of individuals have detectable C-peptide 2 years from diagnosis. In 11% of subjects, there was no significant fall from baseline by 2 years. There was a biphasic decline in C-peptide; the C-peptide slope was −0.0245 pmol/mL/month (95% CI −0.0271 to −0.0215) through the first 12 months and −0.0079 (−0.0113 to −0.0050) from 12 to 24 months (P \u3c 0.001). This pattern of fall in C-peptide over time has implications for understanding trial results in which effects of therapy are most pronounced early and raises the possibility that there are time-dependent differences in pathophysiology. The robust data on the C-peptide obtained under clinical trial conditions should be used in planning and interpretation of clinical trials
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