Factors influencing online orthopedic doctor–patient consultations

Abstract Background Online doctor–patient consultation is a new option for orthopedic patients in China to obtain a diagnosis and treatment advice. This study explores the factors associated with online consultation to formulate operational guidelines for managing online consultations in an online medical community (OMC). Methods An empirical model was developed to identify the factors that influence online orthopedic doctor–patient consultations in an OMC while focusing on the perceived value of and perceived trust in online consultations. The moderating effects of different risk categories of orthopedic diseases were also considered. Data from 339 feedback surveys from orthopedic patients who used online consultation services and Stata software version 14.0 were used to estimate the model parameters and test the robustness of the empirical model. Results Of those who completed the feedback surveys, 53.42% were female patients, 82.27% were between 18 and 60 years old, and 61.98% sought consultations online more than 2 times per year. Model analysis demonstrated that the regression coefficients of the perceived value of and perceived trust in online consultations are 0.489 (p < 0.01) and 0.505 (p < 0.01), respectively. The interaction coefficient between disease risk and perceived value is 0.336 (p < 0.01), and the interaction coefficient between disease risk and perceived trust is − 0.389 (p < 0.01). Conclusions Orthopedic patients’ perceived value of and perceived trust in online consultations in an OMC can significantly influence their intention to seek online disease diagnosis and treatment consultations. The effects of perceived value and perceived trust on patients' intention to consult vary significantly across different disease risk categories. Therefore, enhancing the perceived value and perceived trust of orthopedic patients is an important component of OMC operation and management.

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Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers

Statistical Methods in Medical Research ◽

10.1177/0962280220921553 ◽

2020 ◽

Vol 29 (11) ◽

pp. 3179-3191

Author(s):

Cai Wu ◽

Liang Li ◽

Ruosha Li

Keyword(s):

Kidney Disease ◽

Cumulative Incidence ◽

Disease Risk ◽

Predictor Variable ◽

Cumulative Incidence Function ◽

Competing Risk ◽

Model Parameters ◽

Dynamic Prediction ◽

Risk Of Death ◽

Incidence Function

The cause-specific cumulative incidence function quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted cumulative incidence function by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation-based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted cumulative incidence function. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.

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An Empirical Model for Estimating Soil Thermal Diffusivity from Texture, Bulk Density, and Degree of Saturation

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0131.1 ◽

2018 ◽

Vol 19 (2) ◽

pp. 445-457 ◽

Cited By ~ 7

Author(s):

Xiaoting Xie ◽

Yili Lu ◽

Tusheng Ren ◽

Robert Horton

Keyword(s):

Thermal Diffusivity ◽

Bulk Density ◽

Empirical Model ◽

Soil Texture ◽

Degree Of Saturation ◽

Model Parameters ◽

Near Surface ◽

New Model ◽

Soil Thermal Diffusivity ◽

Mean Square Errors

Abstract Soil thermal diffusivity κ is an essential parameter for studying surface and subsurface heat transfer and temperature changes. It is well understood that κ mainly varies with soil texture, water content θ, and bulk density ρb, but few models are available to accurately quantify the relationship. In this study, an empirical model is developed for estimating κ from soil particle size distribution, ρb, and degree of water saturation Sr. The model parameters are determined by fitting the proposed equations to heat-pulse κ data for eight soils covering wide ranges of texture, ρb, and Sr. Independent evaluations with published κ data show that the new model describes the κ(Sr) relationship accurately, with root-mean-square errors less than 0.75 × 10−7 m2 s−1. The proposed κ(Sr) model also describes the responses of κ to ρb changes accurately in both laboratory and field conditions. The new model is also used successfully for predicting near-surface soil temperature dynamics using the harmonic method. The results suggest that this model provides useful estimates of κ from Sr, ρb, and soil texture.

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An NGA Empirical Model for Estimating the Horizontal Spectral Values Generated by Shallow Crustal Earthquakes

Earthquake Spectra ◽

10.1193/1.2924362 ◽

2008 ◽

Vol 24 (1) ◽

pp. 217-242 ◽

Cited By ~ 124

Author(s):

I. M. Idriss

Keyword(s):

Empirical Model ◽

Standard Error ◽

Strike Slip ◽

Model Parameters ◽

Ground Acceleration ◽

Site Specific ◽

Crustal Earthquakes ◽

Average Shear ◽

Error Terms ◽

New Generation

An empirical model for estimating the horizontal pseudo absolute spectral accelerations (PSA) generated by shallow crustal earthquakes is presented in this paper. The model was selected to be simple and the model parameters were estimated using the recordings gathered as part of the New Generation Attenuation (NGA) project. These parameters are presented for sites with an average shear wave velocity in the upper 30 m, VS30>900 m/s, and for sites with 450 m/s≤ VS30≤900 m/s. Site-specific dynamic response calculations are recommended for estimating spectral ordinates for sites with VS30≤180 m/s. Parameters for sites with 180 m/s< VS30<450 m/s are not included in this paper. The median values of peak horizontal ground acceleration (PGA) and PSA for short periods are on the order of 15% to 20% lower for strike slip events and 30% to 40% lower for reverse events than those calculated using pre-NGA relationships. The differences decrease significantly at longer periods. The minimum values of the standard error terms (for moment magnitude, M≥7.5) are about 15% to 30% larger and the maximum values of the standard error terms (for M≤5) are about 2% to 12% larger than the pre-NGA values.

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A Hybrid Semi-empirical Model for Lean Blow-Out Limit Predictions of Aero-engine Combustors

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4028394 ◽

2014 ◽

Vol 137 (3) ◽

Cited By ~ 11

Author(s):

Bin Hu ◽

Yong Huang ◽

Jianzhong Xu

Keyword(s):

Experimental Data ◽

Numerical Simulation ◽

Empirical Model ◽

The Other ◽

Model Parameters ◽

Model Parameter ◽

Other Hand ◽

Aero Engine ◽

Semi Empirical

According to the Lefebvre's model and flame volume (FV) concept, an FV model about lean blow-out (LBO) was proposed by authors in early study. On the other hand, due to the model parameter (FV) contained in FV model is obtained based on the experimental data, FV model could only be used in LBO analysis instead of prediction. In view of this, a hybrid FV model is proposed that combines the FV model with numerical simulation in the present study. The model parameters contained in the FV model are all estimated from the simulated nonreacting flows. Comparing with the experimental data for 11 combustors, the maximum and average uncertainties of hybrid FV model are ±16% and ±10%.

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Time dependence of the luminescence intensity in CdBr2: AgCl,PbBr2 crystals under N2-laser excitation at room temperature

Materials Science-Poland ◽

10.2478/s13536-014-0232-y ◽

2014 ◽

Vol 32 (4) ◽

pp. 604-609 ◽

Cited By ~ 1

Author(s):

I. Bolesta ◽

B. Kalivoshka ◽

I. Karbovnyk ◽

V. Lesivtsiv ◽

I. Novosad ◽

...

Keyword(s):

Comparative Analysis ◽

Cross Section ◽

Empirical Model ◽

Room Temperature ◽

Laser Excitation ◽

Photochemical Reactions ◽

Luminescence Decay ◽

Model Parameters ◽

Suggested Model ◽

Optical Luminescence

AbstractResults of optical-luminescence studies of polydoped photochromic CdBr2: AgCl,PbBr2 crystals are presented. It is shown that the luminescence decrease vs. time under N2-laser excitation in the range of A-band of Pb2+ absorption is due to photochemical reactions. The empirical model describing the decrease of the luminescence related to silver impurities due to photochemical processes is suggested. Model parameters (trapping cross-section — σ — and the amount of centres destroyed by irradiation — β) were determined using the comparative analysis of experimental and calculated luminescence decay curves.

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Online identification of semi-empirical model parameters for PEMFCs

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2014.10.045 ◽

2014 ◽

Vol 39 (36) ◽

pp. 21165-21176 ◽

Cited By ~ 40

Author(s):

K. Ettihir ◽

L. Boulon ◽

M. Becherif ◽

K. Agbossou ◽

H.S. Ramadan

Keyword(s):

Empirical Model ◽

Model Parameters ◽

Online Identification ◽

Semi Empirical

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The Uncertainty of Cardiovascular Disease Risk Calculation—What is the Best Risk Model for the Individual?

Journal of Engineering and Science in Medical Diagnostics and Therapy ◽

10.1115/1.4039103 ◽

2018 ◽

Vol 1 (2) ◽

Author(s):

Ronald S. LaFleur ◽

Laura S. Goshko

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Clinical Decision Making ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Patient Specific ◽

Model Parameters ◽

Risk Models ◽

The Individual

Cardiovascular disease (CVD) continues to be a leading cause of death. Accordingly, risk models attempt to predict an individual's probability of developing the disease. Risk models are incorporated into calculators to determine the risk for a number of clinical conditions, including the ten-year risk of developing CVD. There is significant variability in the published models in terms of how the clinical measurements are converted to risk factors as well as the specific population used to determine b-weights of these risk factors. Adding to model variability is the fact that numbers are an imperfect representation of a person's health status. Acknowledgment of uncertainty must be addressed for reliable clinical decision-making. This paper analyzes 35 published risk calculators and then generalizes them into one “Super Risk formula” to form a common basis for uncertainty calculations to determine the best risk model to use for an individual. Special error arithmetic, the duals method, is used to faithfully propagate error from model parameters, population averages and patient-specific clinical measures to one risk number and its relative uncertainty. A set of sample patients show that the “best model” is specific to the individual and no one model is appropriate for every patient.

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Use of an Empirical Model to Estimate Leaf Wetness Duration for Operation of a Disease Warning System Under a Shade in a Ginseng Field

Plant Disease ◽

10.1094/pdis-08-14-0790-sr ◽

2016 ◽

Vol 100 (1) ◽

pp. 25-31 ◽

Cited By ~ 3

Author(s):

Kyu Jong Lee ◽

Je Yong Kang ◽

Dong Yun Lee ◽

Soo Won Jang ◽

Semi Lee ◽

...

Keyword(s):

Empirical Model ◽

Disease Risk ◽

Fuzzy Model ◽

Warning System ◽

Weather Data ◽

Effective Control ◽

Leaf Wetness ◽

Spray Application ◽

Leaf Wetness Duration ◽

Foliar Diseases

Ginseng foliar diseases are typically controlled by spray application using periodic schedules. Few disease warning systems have been used for effective control of ginseng foliar diseases because ginseng is grown under shade nettings, which makes it difficult to obtain weather data for operation of the disease warning system. Using weather data measured outside the shade as inputs to an empirical leaf wetness duration (LWD) model, LWD was estimated to examine if operation of a disease warning system would be feasible for control of ginseng foliar diseases. An empirical model based on a fuzzy logic system (fuzzy model) was used to estimate LWD at two commercial ginseng fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012. Accuracy of LWD estimates was assessed in terms of mean error (ME) and mean absolute error (MAE). The fuzzy model tended to overestimate LWD during dew eligible days whereas it tended to underestimate LWD during rainfall eligible days. Still, daily disease risk ratings of the TOM-CAST disease warning system, which are derived from estimates of wetness duration and temperature, had a tendency to coincide with that derived from measurements of weather variables. As a result, spray advisory dates for the TOM-CAST disease warning system were predicted within ±3 days for about 78% of time windows during which the action threshold for spray application was reached. This result suggested that estimates of LWD using an empirical model would be helpful in control of a foliar disease in a ginseng field. It was also found that a spray application time model using meteorological observations may prove successful without the requirement of leaf wetness sensors within the field. Development of empirical correction schemes to the fuzzy model and a physical model for LWD estimation in a ginseng field could improve accuracy of LWD estimates and, as a result, spray advisory date prediction, which merits further studies.

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