The Three-Function Model of the Medical Interview

2015 ◽  
pp. 65-88 ◽  
Author(s):  
Julian Bird ◽  
Steven A. Cohen-Cole
Keyword(s):  
Medical Interview ◽  
Function Model

Components of executive function model regional prefrontal volumes.

2019 ◽  
Vol 33 (7) ◽  
pp. 1007-1019 ◽  
Author(s):  
Ryan A. Mace ◽  
Abigail B. Waters ◽  
Kayle S. Sawyer ◽  
Taylor Turrisi ◽  
David A. Gansler
Keyword(s):  
Executive Function ◽  
Function Model

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

scholarly journals Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors

Biostatistics ◽  
2019 ◽  
Author(s):  
Dane R Van Domelen ◽  
Emily M Mitchell ◽  
Neil J Perkins ◽  
Enrique F Schisterman ◽  
Amita K Manatunga ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Lower Cost ◽  
Cost Effective ◽  
Ratio Estimation ◽  
Statistical Efficiency ◽  
Function Model ◽  
Processing Error ◽  
Wide Range ◽  
Pooled Samples

SUMMARYMeasuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

scholarly journals Difference in the general medicine in-training examination score between community-based hospitals and university hospitals: a cross-sectional study based on 15,188 Japanese resident physicians

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuji Nishizaki ◽  
Keigo Nozawa ◽  
Tomohiro Shinozaki ◽  
Taro Shimizu ◽  
Tomoya Okubo ◽  
...  
Keyword(s):  
Physical Examination ◽  
General Medicine ◽  
Cross Sectional Study ◽  
Medical Interview ◽  
Sectional Study ◽  
University Hospitals ◽  
Community Based ◽  
Cross Sectional ◽  
Clinical Competencies ◽  
Clinical Procedures

Abstract Background The general medicine in-training examination (GM-ITE) is designed to objectively evaluate the postgraduate clinical competencies (PGY) 1 and 2 residents in Japan. Although the total GM-ITE scores tended to be lower in PGY-1 and PGY-2 residents in university hospitals than those in community-based hospitals, the most divergent areas of essential clinical competencies have not yet been revealed. Methods We conducted a nationwide, multicenter, cross-sectional study in Japan, using the GM-ITE to compare university and community-based hospitals in the four areas of basic clinical knowledge“. Specifically, “medical interview and professionalism,” “symptomatology and clinical reasoning,” “physical examination and clinical procedures,” and “disease knowledge” were assessed. Results We found no significant difference in “medical interview and professionalism” scores between the community-based and university hospital residents. However, significant differences were found in the remaining three areas. A 1.28-point difference (95% confidence interval: 0.96–1.59) in “physical examination and clinical procedures” in PGY-1 residents was found; this area alone accounts for approximately half of the difference in total score. Conclusions The standardization of junior residency programs and the general clinical education programs in Japan should be promoted and will improve the overall training that our residents receive. This is especially needed in categories where university hospitals have low scores, such as “physical examination and clinical procedures.”

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