Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal.
The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.
This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
Stephen V. Gliske, Zachary T. Irwin, Cynthia Chestek, Garnett L. Hegeman, Benjamin Brinkmann, Oren Sagher, Hugh J. L. Garton, Greg A. Worrell, William C. Stacey. "Variability in the location of High Frequency Oscillations during prolonged intracranial EEG recordings." Nature Communications. https://doi.org/10.1038/s41467-018-04549-2
The dataset represents the complete search strategies for all literature databases searched during the systematic review. The Endnote and Excel files of all citations considered for inclusion in the review are also included.
Contained within is a subset of the larger dataset collected in La Paz, Bolivia in 2014. This data contains the analytic dataset (cross-sectional/descriptive) that includs the PACIC, Morisky, PHQ8, AUDIT, and a subset of socidemographic characteristics for NCD patients in La Paz.
Introduction: Diagnostic testing is common in the emergency department. The value of some testing is questionable. The purpose of this study was to assess how varying levels of benefit, risk, and costs influenced an individual’s desire to have diagnostic testing.
Methods: A survey through Amazon Mechanical Turk presented hypothetical clinical situations: low risk chest pain and minor traumatic brain injury. Each scenario included three given variables (benefit, risk, and cost), that was independently randomly varied over four possible values (0.1%, 1%, 5%, 10% for benefit and risk and $0, $100, $500, and $1000 for the individual’s personal cost for receiving the test). Benefit was defined as the probability of finding the target disease (traumatic intracranial hemorrhage or acute coronary syndrome).
Results: A total of 1000 unique respondents completed the survey. Increasing benefit from 0.1% to 10%, the percent of respondents who accepted a diagnostic test went from 28.4% to 53.1%. [OR: 3.42 (2.57-4.54)] As risk increased from 0.1% to 10%, this number decreased from 52.5% to 28.5%. [OR: 0.33 (0.25-0.44)] Increasing cost from $0 to $1000 had the greatest change of those accepting the test from 61.1% to 21.4%, respectively. [OR: 0.15 (0.11-0.2)]
Conclusions: The desire for testing was strongly sensitive to the benefits, risks and costs. Many participants wanted a test when there was no added cost, regardless of benefit or risk levels, but far fewer elected to receive the test as cost increased incrementally. This suggests that out of pocket costs may deter patients from undergoing diagnostic testing with low potential benefit.