In a sensitive cochlea, the basilar membrane response to transient excitation of any kind--normal acoustic or artificial intracochlear excitation--consists of not only a primary impulse but also a coda of delayed secondary responses with varying amplitudes but similar spectral content around the characteristic frequency of the measurement location. The coda, sometimes referred to as echoes or ringing, has been described as a form of local, short term memory which may influence the ability of the auditory system to detect gaps in an acoustic stimulus such as speech. Depending on the individual cochlea, the temporal gap between the primary impulse and the following coda ranges from once to thrice the group delay of the primary impulse (the group delay of the primary impulse is on the order of a few hundred microseconds). The coda is physiologically vulnerable, disappearing when the cochlea is compromised even slightly. The multicomponent sensitive response is not yet completely understood. We use a physiologically-based, mathematical model to investigate (i) the generation of the primary impulse response and the dependence of the group delay on the various stimulation methods, (ii) the effect of spatial perturbations in the properties of mechanically sensitive ion channels on the generation and separation of delayed secondary responses. The model suggests that the presence of the secondary responses depends on the wavenumber content of a perturbation and the activity level of the cochlea. In addition, the model shows that the varying temporal gaps between adjacent coda seen in experiments depend on the individual profiles of perturbations. Implications for non-invasive cochlear diagnosis are also discussed.
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.
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.
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.
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.