Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection

When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance

Gamma-Delta (gammadelta) (γδ) T-cell Lymphoma - Another Case Unclassifiable by World Health Organization Classification: a Case Report

BACKGROUND: We present a case of gamma-delta T-cell lymphoma that does not fit the current World Health Organization classifications. CASE PRESENTATION: A 74-year-old Caribbean-American woman presented with lymphocytosis, pruritus, and non-drenching night sweats. Bone marrow and peripheral blood analyses both confirmed the diagnosis of gamma-delta T-cell lymphoma. An axillary lymph node biopsy was negative for lymphoma. Clinically absent hepatosplenomegaly and skin lesions with biopsy-proven gamma-delta T-cell lymphoma suggest that she is unclassifiable within the current classification system. CONCLUSIONS: We believe this is a case of not otherwise specified gamma-delta T-cell lymphoma. Accumulation of these rare not otherwise specified cases will be important for future classification which further defines the biology of this disease

Exoplanets Around Red Giants: Distribution and Habitability

As the search for exoplanets continues, more are being discovered orbiting Red Giant stars. We use current data from the NASA Exoplanet Archive to investigate planet distribution around Red Giant stars and their presence in the host's habitable zone. As well, we update the power law relation between planet mass and stellar radius found in previous studies and provide more detailed investigations on this topic. Ten Red Giant-hosted exoplanets are found to be in the optimistically calculated habitable zone, five of which are in a more conservatively calculated habitable zone. We believe additional exoplanets can be found in habitable zones around Red Giants using the direct imaging and other methods, along with more powerful detection instrumentation

Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine

One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we develop novel diagnostic reasoning prompts to study whether LLMs can perform clinical reasoning to accurately form a diagnosis. We find that GPT4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy. This is significant because an LLM that can use clinical reasoning to provide an interpretable rationale offers physicians a means to evaluate whether LLMs can be trusted for patient care. Novel prompting methods have the potential to expose the black box of LLMs, bringing them one step closer to safe and effective use in medicine

Chronic viral infection promotes sustained Th1-derived immunoregulatory IL-10 via BLIMP-1

During the course of many chronic viral infections, the antiviral T cell response becomes attenuated through a process that is regulated in part by the host. While elevated expression of the immunosuppressive cytokine IL-10 is involved in the suppression of viral-specific T cell responses, the relevant cellular sources of IL-10, as well as the pathways responsible for IL-10 induction, remain unclear. In this study, we traced IL-10 production over the course of chronic lymphocytic choriomeningitis virus (LCMV) infection in an IL-10 reporter mouse line. Using this model, we demonstrated that virus-specific T cells with reduced inflammatory function, particularly Th1 cells, display elevated and sustained IL-10 expression during chronic LCMV infection. Furthermore, ablation of IL-10 from the T cell compartment partially restored T cell function and reduced viral loads in LCMV-infected animals. We found that viral persistence is needed for sustained IL-10 production by Th1 cells and that the transcription factor BLIMP-1 is required for IL-10 expression by Th1 cells. Restimulation of Th1 cells from LCMV-infected mice promoted BLIMP-1 and subsequent IL-10 expression, suggesting that constant antigen exposure likely induces the BLIMP-1/IL-10 pathway during chronic viral infection. Together, these data indicate that effector T cells self-limit their responsiveness during persistent viral infection via an IL-10-dependent negative feedback loop.This work was supported by an Australian NHMRC Overseas Biomedical Postdoctoral Fellowship (to I.A. Parish); a Yale School of Medicine Brown-Coxe Postdoctoral Fellowship (to I.A. Parish); the Alexander von Humboldt Foundation (SKA2010, to P.A. Lang); a CIHR grant (to P.S. Ohashi); and by the Howard Hughes Medical Institute and NIH grant RO1AI074699 (to S.M. Kaech). P.S. Ohashi holds a Canada Research Chair in Autoimmunity and Tumor immunity