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At least 19 recordsLinked to original sources
PubMed1977

Prior time uncertainty reduction of foreperiod duration under two different levels of event uncertainty in schizophrenia.

Schizophrenic and alcoholic Ss judged stimulus durations of .50 and .55 seconds. Stimulus was 1, 3 or 5 dark dots and was preceded by variable foreperiod duration of 1, 3 or 5 seconds. Judgment of stimulus duration was found to be a monotonically increasing function of foreperiod duration. When pitch of sound was correlated with foreperiod duration so that it functioned as prior information, such monotonic function was eliminated for schizophrenics only when the numerosity of dots was constant from trial to trial. The effect of prior information as to foreperiod duration was negligible for both schizophrenics and alcoholics when the numerosity of dots was variable from trial to trial. Uniqueness of schizophrenia was shown to be associated with prior time uncertainty reduction of foreperiod duration in the context of minimal event uncertainty of stimulus.

Acoustic Stimulation↗
PubMed1977

Prior time uncertainty reduction of foreperiod duration under two different levels of event uncertainty in process and reactive schizophrenia.

Process and reactive schizophrenic Ss judged stimulus durations of .50 and .55 seconds. Stimulus was 1, 3 or 5 dark dots and was preceded by variable foreperiod duration of 1, 3 or 5 seconds. Judgment of stimulus duration was found to be a monotonically increasing function of foreperiod duration. When pitch of the auditory warning signal was correlated with foreperiod duration so that it functioned as prior time information, such monotonic function was eliminated for process schizophrenics only when the numerosity of dots was constant from trial to trial. The effect of prior time information that concerned foreperiod duration was negligible for both process and reactive schizophrenics when the numerosity of dots was variable from trial to trial. Uniqueness of process schizophrenia was shown to be associated with prior time uncertainty reduction of foreperiod duration in the context of minimal event uncertainty of stimulus.

Adult↗
PubMed1977

Prior event uncertainty reduction under two different levels of time uncertainty of foreperiod duration in schizophrenia.

Schizophrenic and alcoholic Ss judged stimulus durations of .50 and .55 seconds. Stimulus was 1, 3 or 5 dark dots and was preceded by variable foreperiod duration of 1, 3 or 5 seconds. Judgment of stimulus duration was found to be a monotonically increasing function of both foreperiod duration and numerosity of dots. When pitch of sound, which functioned as a warning signal, was correlated with numerosity of dots, the monotonic relation between numerosity of dots and estimation of stimulus duration was eliminated for alcoholics, but not for schizophrenics, only when foreperiod duration was constant from trial to trial. The effect of prior information as to numerosity of dots was negligible for both schizophrenics and alcoholics when foreperiod duration was variable from trial to trial.

Adult↗
PubMed2025

Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare.

The integration of machine learning (ML) into healthcare is accelerating, driven by the proliferation of biomedical data and the promise of data-driven clinical support. A key challenge in this context is managing the pervasive uncertainty inherent in medical reasoning and decision-making. Despite its recognized importance, uncertainty is often underrepresented in the design and evaluation of clinical AI systems. Here we report an editorial overview of a special issue dedicated to uncertainty modeling in medical AI, which gathers theoretical, methodological, and practical contributions addressing this critical gap. Across these works, authors reveal that fewer than 4% of studies address uncertainty explicitly, and propose alternative design principles-such as optimizing for clinical net benefit or embedding explainability with confidence estimates. Notable contributions include the RelAI system for real-time prediction reliability, empirical findings on how uncertainty communication shapes clinical interpretation, and benchmarks for out-of-distribution detection in tabular data. Furthermore, this issue highlights the use of causal reasoning and anomaly detection to enhance system robustness and accountability. Together, these studies argue that representing, communicating, and operationalizing uncertainty are essential not only for clinical safety but also for building trust in AI-driven care. This special issue thus repositions uncertainty from a limitation to a foundational asset in the responsible deployment of ML in healthcare.

Machine Learning↗
PubMed2026

Patient experiences of diagnostic uncertainty in musculoskeletal care: a systematic review of qualitative studies.

BACKGROUND: Diagnosis plays a central role in musculoskeletal care. However, establishing a clear diagnosis is often challenging, and diagnostic uncertainty is common. OBJECTIVES: To explore patient experiences of diagnostic uncertainty in musculoskeletal care. METHODS: Five databases (CINAHL, Embase, MEDLINE, AMED, Web of Science) were searched from inception to October 2025. Qualitative studies involving semi-structured interviews with adults receiving care for MSK conditions were included. Methodological quality was appraised using the Joanna Briggs Institute Qualitative Checklist. Data were synthesised using thematic synthesis, and confidence in findings was assessed using the Grading of Recommendations Assessment, Development, and Evaluation Confidence in the Evidence from Reviews of Qualitative Research approach (GRADE-CERQual). RESULTS: Twenty-six studies involving 462 participants were included. Critical appraisal identified 23 studies with varying methodological limitations; all studies were included in the synthesis. Nine descriptive themes were synthesised into three analytical themes: (1) patient expectations and perceived meanings of a diagnosis and interpretations of diagnostic uncertainty; (2) the multi-dimensional experience of diagnostic uncertainty; and (3) the role of contextual factors, particularly communication and the therapeutic relationship, in shaping experiences of diagnostic uncertainty. Using GRADE-CERQual, confidence in these themes was rated as low, moderate and very low, respectively. CONCLUSION: Diagnostic uncertainty is a subjective and multi-dimensional experience shaped in part by patients' expectations and the meanings attributed to diagnosis. Its impact spans predominantly cognitive and affective domains and may influence clinical presentation. Patient-centred communication and strong therapeutic relationships may support patients in navigating diagnostic uncertainty in musculoskeletal care.

Adult↗
PubMed1979

The developmental relation between cognitive stage and the comprehension of speaker uncertainty.

3 hypotheses were tested concerning the developmental relation between children's concepts of physical uncertainty and their comprehension of a speaker's uncertainty. 2 cognitive tasks of physical uncertainty were used to assign 56 subjects (aged 5-4 to 17-11) to 1 of 3 cognitive stages. 2 tests for comprehension of speaker uncertainty were administered to all participants. The results indicated that cognitive stage was related to (a) comprehension that a speaker could be uncertain, (b) comprehension that uncertainty could be expressed in different degrees or magnitudes, and (c) the internal consistency of judgments made about the relative degree of uncertainty conveyed by an utterance. These findings are interpreted as evidence for the position that development of cognitive stages is structurally related to comprehension of speech act uncertainty.

Adolescent↗
PubMed2026

Characterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction.

Polygenic risk scores (PRSs) hold promise for precision medicine, yet their clinical translation is hindered by substantial uncertainty in individual risk estimates and often limited agreement in risk stratification across multiple PRSs for the same disease. We develop a unified inferential framework to calibrate PRS point estimates and uncertainties for both quantitative traits and binary phenotypes, and to characterize how PRS accuracy, uncertainty, pairwise correlation jointly determine misclassification and classification inconsistency. We show, both theoretically and empirically, that individual- and population-level misclassification and inconsistency rates are highly predictable in independent datasets. We further evaluate PRS integration and uncertainty-aware probabilistic thresholding strategies that reduce misclassification and improve concordance in risk stratification. Together, these results demonstrate that instability in PRS-based classification is a predictable statistical consequence of uncertainty and establish a principled foundation for incorporating uncertainty into PRS-based risk interpretation, communication, and clinical decision-making.

Journal Article↗
PubMed1979

Genetic counseling--the postcounseling period: I. Parents' perceptions of uncertainty.

To investigate how parents who have had genetic counseling perceive the problems created by being at risk, transcripts of open-ended, semistructured follow-up interviews with 53 counselees were analyzed qualitatively. Rate information, though recalled accurately by parents considering further childbearing, was discounted as impersonal, and subjects overwhelmingly perceived the chance of recurrence in binary form -- it either will or will not happen. By processing rates this way, they simplified probabilistic information and shifted their focus to the implications of being at risk and the potential impact of that which might or might not occur. The many uncertainties they faced, the "consequences" of being at risk that parents felt had to be resolved during the decision-making process, fell into 3 major categories: uncertainty that arose because of the ambiguous impact and meaning of having an affected child; uncertainty about how to make a choice and how others would view it, the burden of decision-making; and uncertainty about their ability to fulfill their roles as parents. These issues were perceived as part of the problem to be resolved and were consolidated into "scenarios" in which the parents "tried out the worst." This analysis of counselees' perceptions of the problems created by being at genetic risk suggests that parents may process the disparate facts of their situation in common ways that emphasize their uncertainty, and it indicates that how parents perceive factual information may be more important in orienting their deliberations than what these facts (diagnosis, prognosis, risks) actually are.

Decision Making↗
PubMed2026

Uncertainty and information management for Lynch syndrome in a genomic screening cohort: Connections to clinical engagement.

OBJECTIVE: Lynch syndrome (LS) is a common hereditary cancer predisposition syndrome. This study explores how individuals learning about LS-related cancer risk through a population genomic screening program appraise, reappraise, and manage uncertainty and engage in medical management. METHODS: We recruited participants with an LS result from a population genomic screening study, purposively sampling based on the characteristics of sex, age, LS gene, and family history of cancer. Semi-structured interviews and chart reviews focused on participants' experiences of learning about the LS result and making management decisions. Thematic analysis and heatmapping were used to explore participant characteristics related to uncertainty management and clinical engagement. RESULTS: Seventeen participants completed interviews, describing uncertainty discrepancies or ambivalent appraisals. Most participants who appraised their LS risk as a threat had high clinical engagement. All participants who had lower clinical engagement received misinformation, had little support from clinicians, or had misunderstandings about LS. CONCLUSION: Accurate information from trusted sources is critical to support uncertainty management and subsequent engagement in clinical risk management choices. PRACTICE IMPLICATIONS: Ensuring primary care providers have the information and tools to provide patients with accurate information may support recommended clinical engagement, thereby improving health outcomes for this population.

Clinical Engagement↗
PubMed1977

Uncertainty, as defined by the contingency between environmental events, and the adrenocortical response of the rat to electric shock.

The pituitary-adrenal system is thought to be sensitive to the degree of uncertainty in a situation. In addition, there is some question whether the pituitary-adrenal system can be conditioned in a Pavlovian sense. Three experiments are reported here. The first and third sought to define uncertainty in terms of conditioned stimulus-unconditioned stimulus (CB-US) and US-US contingencies, which vary the amount of information that can be used to predict the occurrence of discrete shocks. The second experiment examined the possibility that the adrenocortical system was subject to the laws of Pavlovian conditioning, by using a conditioned emotional response paradigm. The results showed that the magnitude of the Pituitary-adrenal response varied in a curvilinear manner along the dimension of uncertainty. Very low and very high degrees of uncertainty resulted in greater corticosterone elevations than did moderate levels. No evidence for Pavlovian conditioning of the adrencortical system was found, although behavioral measures showed fear conditioning. The data presented were supportive of the hypothesis that the pituitary-adrenal response reflects the operation of an arousal system.

Animals↗
PubMed2026

Toward Class Imbalance and Uncertainty in Powder XRD Analysis: A Dual-Channel Fusion Network for Space Group Classification.

Accurate identification of space groups from powder X-ray diffraction (pXRD) is essential for understanding crystal structures and accelerating materials discovery. However, this task remains highly challenging due to inherent peak overlap, experimental noise, and the complexity of the 230-class classification problem. To address the critical issues of class imbalance and data scarcity, we first design a general physics-informed data augmentation pipeline. We then propose a dual-channel fusion uncertainty-aware network (DFUN) for automated space group classification. The DFUN architecture integrates two complementary feature representations: convolutional features extracted directly from raw diffraction profiles and domain-specific peak descriptors. These distinct representations are adaptively fused through a gating mechanism. Furthermore, to mitigate the inherent long-tailed distribution of crystallographic data, we employ a hybrid loss function that combines Focal Loss with Label Smoothing. Finally, we incorporate Monte Carlo Dropout to provide predictive uncertainty estimation, thereby enabling not only accurate classification but also a crucial assessment of the model's reliability. Evaluated on large-scale simulated data and two public data sets (opXRD and RRUFF), DFUN outperforms the evaluated baseline methods across the reported metrics. The framework also provides uncertainty-aware predictions, establishing DFUN as a robust and interpretable solution for high-throughput automated crystallographic analysis from powder diffraction.

Uncertainty↗
PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗
PubMed1977

Psychological differentiation, event uncertainty, and heart rate.

Psychological differentiation and uncertainty about receiving a painful noise were examined for their effects on heart rate during the anticipatory, impact and recovery phases of the tone presentation. Psychologically differentiated and nondifferentiated subjects were randomly assigned to three event uncertainty conditions (5 percent, 50 percent, 95 percent probability of noise). Subjects were informed of the probability of receiving the noise, as well as the time of occurrence as indicated by a sequentially numbered visual display. Subjects received the noise on the second of the experiment's two trials. Cognitive style and event uncertainty interacted during the anticipatory phase--i.e., differentiated subjects showed a monotonic increase in heart rate with increasing probability of receiving the noise that represents preparation for instrumental activity. Results are consistent with the theory that meaningful personality-stress relationships may be obtained when examining stimulus-oriented dispositions.

Acoustic Stimulation↗
PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗
PubMed1979

Effects of listener uncertainty on articulatory inconsistency.

This investigation determined the effects of listener uncertainty on articulatory inconsistency. Subjects were 15 children between three and five years of age. Each subject was tested to find a set of 45 pictures to which articulatory responses would contain sound errors on: /f,v,th,s,ts,dz/. After one week, articulatory responses to these 45 stimuli elicited by traditional picture naming techniques were compared to productions of the same words elicited in an experimental communication setting. The number of sound errors decreased significantly (p less than or equal to 0.01) in the experimental communication setting when the listener pretended to be uncertain of what the speaker said. This finding was interpreted to mean that listener uncertainty may increase the effectiveness of articulatory remediation procedures if included in treatment programs.

Articulation Disorders↗
PubMed2026

Navigating uncertainties and evidence gaps in adjuvant therapy for premenopausal women with HR+/HER2- breast cancer.

Premenopausal women with hormone receptor-positive (HR+)/HER2-negative early breast cancer represent a clinically distinct population, characterized by more aggressive tumor biology, unique survivorship concerns, and complex treatment decision-making. Although evidence from dedicated trials and subgroup analyses is available, management remains challenging because data are heterogeneous, evolving, and often extrapolated from broader populations that include predominantly postmenopausal women. Adjuvant endocrine therapy, with the addition of CDK4/6 inhibitors in selected higher-risk patients, remains the cornerstone of treatment; however major uncertainties persist regarding the optimal use of ovarian function suppression, the interpretation of genomic assays to inform chemotherapy decisions, the selection of candidates for extended endocrine therapy, and the management of adherence, treatment-related toxicities, pregnancy-related issues, and survivorship concerns. Here, we synthesize current evidence across these domains and propose a pragmatic clinical framework to support individualized treatment strategies and optimize care for this population in the contemporary therapeutic landscape.

HR+/HER2- early breast cancer↗
PubMed2026

GiantHost: a domain-adaptive and uncertainty-aware framework for giant virus host prediction.

MOTIVATION: Nucleocytoplasmic large DNA viruses (NCLDVs) play crucial roles in global ecosystems. Although metagenomics has vastly accelerated the discovery of novel NCLDVs, predicting their hosts from fragmented contigs remains a critical bottleneck, with no dedicated end-to-end computational tools currently available. Addressing this gap requires overcoming three fundamental challenges: the extreme scarcity of labeled reference genomes, the severe domain shift between laboratory isolates and diverse environmental metagenomes, and the inability of traditional deterministic models to quantify prediction uncertainty-a crucial requirement for reliable ecological profiling where novel, divergent viruses are prevalent. RESULTS: We present GiantHost, the first NCLDV host prediction tool with domain adaptation and uncertainlty awareness. GiantHost employs a dual-tower neural network to integrate dense genome traits and sparse GVOG profiles, allowing better integration of heterogeneous features. To overcome label scarcity and domain shift, we leverage 1400 environmental viral genomes (GVMAGs) via semi-supervised multi-task learning and Domain Adversarial Neural Networks (DANN), effectively bridging the distributional gap between RefSeq and environmental data. Additionally, GiantHost incorporates Conformal Prediction (CP) to output statistically guaranteed prediction sets rather than overconfident single labels. Evaluated under rigorous genome-level cross-validation, GiantHost demonstrates robust predictive power. Applied to the Tara Ocean dataset, GiantHost successfully captured the vertical stratification of NCLDV hosts-revealing a depth-dependent decline of phytoplankton-infecting viruses and a relative enrichment of Amoebozoa-infecting viruses in the mesopelagic zone. AVAILABILITY: The source code of GiantHost is available via: https://github.com/FuchuanQu/GiantHost.

Giant Viruses↗