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Epicuro Kishore
Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well-known models, such as univariate and multivariate versions of the Rasch model and the two-parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL Internet-based testing.
Introduction: Information on laboratory test availability and current testing scope among general hospitals in Kenya is not readily available. We sought to explore the reporting trends and test availability within clinical laboratories in Kenya over a 24-months period through analysis of the laboratory data reported in the District Health Information System (DHIS2).
Methods: Monthly hospital laboratory testing data were extracted from the Kenyan DHIS2 between January 2018 and December 2019. We used the national laboratory testing summary tool (MoH 706) to identify the tests of interest among 204 general hospitals in Kenya. A local practitioner panel consisting of individuals with laboratory expertise was used to classify the tests as common and uncommon. We compared the tests on the MoH 706 template with the Essential Diagnostic List (EDL) of the World Health Organisation and further reclassified them into test categories based on the EDL for generalisability of our findings. Evaluation of the number of monthly test types reported in each facility and the largest number of tests ever reported in any of the 24 months were used to assess test availability and testing scope, respectively.
Results: Out of the 204 general hospitals assessed, 179 (179/204) reported at least one of the 80 tests of interest in any of the 24 months. Only 41% (74/179) of the reporting hospitals submitted all their monthly DHIS2 laboratory reports for the entire 24 months. The median testing capacity across the hospitals was 40% with a wide variation in testing scope from one hospital laboratory to another (% IQR: 33.8-51.9). Testing scope was inconsistent within facilities as indicated by often large monthly fluctuations in the total number of recommended and EDL tests reported. Tests of anatomical pathology and cancer were the least reported with 4 counties' hospitals not reporting any cancer or anatomical pathology tests for the entire 24 months.
Conclusion: The current reporting of laboratory testing information in DHIS2 is poor. Monitoring access and utilisation of laboratory testing across the country would require significant improvements in consistency and coverage of routine laboratory test reporting in DHIS2. Nonetheless, the available data suggest unequal and intermittent population access to laboratory testing provided by general hospitals in Kenya.
This study proposes and evaluates a general diagnostic classification model (DCM) for rating scales. We applied the proposed model to a dataset to compare its performance with traditional DCMs for polytomous items. We also conducted a simulation study based on the applied study condition in order to evaluate the parameter recovery of the proposed model. The findings suggest that the proposed model shows promise for (1) accommodating much smaller sample sizes by reducing a large number of parameters for estimation; (2) obtaining item category response probabilities and individual scores very similar to those from a traditional saturated model; and (3) providing general item information that is not available in traditional DCMs for polytomous items.
where tm is a category threshold parameter for option m across all items. Comparing Eq. 1 to Eq. 2, the category threshold parameter bim is decomposed into two components: bi (the item general location parameter) and tm (the threshold parameter for category m common to all items). In other words, instead of freely estimating one parameter at the intersection of each item and each threshold, the MGRM has only one parameter for each threshold, and that set of threshold parameters is applied to all items. This assumes that the relative difficulty between steps is held constant across items. For example, Likert scales with the same response categories across items (e.g., strongly disagree, disagree, agree, strongly agree) may naturally have this feature, while other item types may not.
Comparing the RSDM in Eq. 4 to the NRDM in Eq. 3, we can see that the intercept parameter λ0, i, m is decomposed into λ0, i (an item general location parameter for the intercept) and \( \sum_v=1^V\lambda_0,m,vw_iv \) (a threshold parameter for category m common to the items measuring attribute set v). The parameters for the main effects and the interaction effects are also similarly decomposed into those two parts.
For evaluation of the model fit, we used the leave-one-out cross-validation (LOO) method, which is designed for evaluating the predictive accuracy of a Bayesian model with simulated parameter values (Gelman, Hwang, & Vehtari, 2014; Vehtari, Gelman, & Gabry, 2017; Yao, Vehtari, Simpson, & Gelman, 2018). As was pointed out in Vehtari et al., the LOO has many advantages over traditional simpler indices, such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), and deviance information criterion (DIC). In this study, we implemented the Pareto-smoothed importance-sampling algorithm (Vehtari et al., 2017) to compute the LOO, and report the expected log predictive density (ELPD) and LOO information criterion (LOOIC) for both the RSDM and the NRDM. The ELPD is computed as
The RSDM was developed as a constrained version of the NRDM, built on the practices of sharing category threshold parameters (Andrich, 1978; Muraki, 1990) and representing dimensions with latent classes in DCMs (Rupp & Templin, 2008; Templin & Hoffman, 2013). The simulation study demonstrated that the RSDM was able to recover parameters and correctly classify individuals under an applied condition. Although we witnessed larger bias and RMSE for category threshold parameters at extreme locations, the estimated attribute profile distribution and attribute classifications were almost ideal. The applied study showed that the RSDM produced very similar item category response probabilities and individual scores with the NRDM. In addition to its 50% smaller model size, the RSDM also offered general item information through an overall intercept and an overall main effect for each item, which is not available in traditional DCMs for polytomous items. Practitioners may find this information useful for item revision, reporting, and score interpretation.
To conclude, although polytomous DCMs have been developed and applied to psychological tests (e.g., Templin & Henson, 2006), the majority of model development and applications of DCMs are thriving in educational testing scenarios. We hope that the RSDM can be useful for classifying individuals on psychological rating scales such as personality tests and diagnostics of behavioral/mental disorders.
Our Imaging Services Department provides diagnostic imaging studies including routine x-ray studies (bone, chest, abdomen), PET/CT scanning, Ultrasound, Nuclear Medicine, Digital Mammography, MRI and CT Scanning.
A registered radiologic technologist who is specially trained in this field performs your x-rays. The Department operates with the direction of Radiologists who are physicians specializing in the diagnosis of disease by means of x-ray and other imaging examinations. For certain procedures, a radiologist will be present. All examinations are interpreted and reported by the radiologist to your physician to help in your diagnosis and treatment.
One of our newest additions is our state of the art PACS (Picture Archive Communication System). This system gives us the capability of digitally capturing all of your studies and processing them for immediate verification and transmission to our Radiologist for viewing on a Diagnostic Workstation. Our PACS allows us to copy your studies to a CD for viewing by any referring physician or facility. The studies copied to CD will allow physicians to manipulate and change viewing preferences, making this a better diagnostic tool than printed films and saving you time when requesting copies of your studies.
If Generate PS Series diagnostics is selected in step 4, Selected Members screen is displayed.
Confirm the target members that Diagnostics is gathered and click Next.
If this option is not selected in step 4, go to step 7.
Diagnosis Index entries containing back-references to Z00.00:
- Admission (for) - see also Encounter (for) examination at health care facility (adult) Z00.00 - see also Examination
- Persons encountering health services NOS