Medicine is diagnosis, treatment and care. To diagnose is to consider the probability of the cause of discomfort experienced by the patient. The physician may face many options and all decisions are liable to uncertainty to some extent. The rational action is to perform selected tests and thereby increase the pre-test probability to reach a superior post-test probability of a particular option. To draw the right conclusions from a test, certain background information about the performance of the test is necessary. We set up a partially artificial dataset with measured results obtained from the laboratory information system and simulated diagnosis attached. The dataset is used to explore the use of contingency tables with a unique graphic design and software to establish and compare ROC graphs. The loss of information in the ROC curve is compensated by a cumulative data analysis (CDA) plot linked to a display of the efficiency and predictive values. A standard for the contingency table is suggested and the use of dynamic reference intervals discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707117 | PMC |
| http://dx.doi.org/10.11613/BM.2018.010101 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Neurofeedback and attention modulate somatosensory alpha oscillations but not pain perception.
PLoS Biol
January 2025
Department of Neurology, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany.
Pain is closely linked to alpha oscillations (8 < 13 Hz) which are thought to represent a supra-modal, top-down mediated gating mechanism that shapes sensory processing. Consequently, alpha oscillations might also shape the cerebral processing of nociceptive input and eventually the perception of pain. To test this mechanistic hypothesis, we designed a sham-controlled and double-blind electroencephalography (EEG)-based neurofeedback study.
View Article and Find Full Text PDFData-driven discovery and parameter estimation of mathematical models in biological pattern formation.
PLoS Comput Biol
January 2025
Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.
View Article and Find Full Text PDFBayesian Inference of Phylogenetic Distances: Revisiting the Eigenvalue Approach.
Bull Math Biol
January 2025
Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis.
View Article and Find Full Text PDFTo compare the effectiveness of injury prevention programs (IPPs) for improving high-risk knee motion patterns in the context of reducing the risk of noncontact anterior cruciate ligament injury. Systematic review with Bayesian network meta-analysis. PubMed, Embase, Web of Science, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature were searched until September 10, 2023.
View Article and Find Full Text PDFBayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART).
Pharm Stat
January 2025
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!