Dataset Information

Integrating personalized medical test contents with XML and XSL-FO.

ABSTRACT: BACKGROUND: In 2004 the adoption of a modular curriculum at the medical faculty in Muenster led to the introduction of centralized examinations based on multiple-choice questions (MCQs). We report on how organizational challenges of realizing faculty-wide personalized tests were addressed by implementation of a specialized software module to automatically generate test sheets from individual test registrations and MCQ contents. METHODS: Key steps of the presented method for preparing personalized test sheets are (1) the compilation of relevant item contents and graphical media from a relational database with database queries, (2) the creation of Extensible Markup Language (XML) intermediates, and (3) the transformation into paginated documents. RESULTS: The software module by use of an open source print formatter consistently produced high-quality test sheets, while the blending of vectorized textual contents and pixel graphics resulted in efficient output file sizes. Concomitantly the module permitted an individual randomization of item sequences to prevent illicit collusion. CONCLUSIONS: The automatic generation of personalized MCQ test sheets is feasible using freely available open source software libraries, and can be efficiently deployed on a faculty-wide scale.

SUBMITTER: Toddenroth D

PROVIDER: S-EPMC3056837 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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Integrating personalized medical test contents with XML and XSL-FO.

Toddenroth Dennis D Dugas Martin M Frankewitsch Thomas T

BMC medical education 20110301

<h4>Background</h4>In 2004 the adoption of a modular curriculum at the medical faculty in Muenster led to the introduction of centralized examinations based on multiple-choice questions (MCQs). We report on how organizational challenges of realizing faculty-wide personalized tests were addressed by implementation of a specialized software module to automatically generate test sheets from individual test registrations and MCQ contents.<h4>Methods</h4>Key steps of the presented method for preparin ...[more]

PMID: 21362187

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Project description:BackgroundMicroarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since microarrays analyze a huge number of genes simultaneously, concerns of multiplicity, i.e. the family wise error rate (FWER) and false discovery rate (FDR), have been raised in testing the data. To deal with these concerns, several compensation methodologies have been proposed, making the tests very conservative to the extent that arbitrary tuning of the threshold has been introduced to relax the conditions. Unexpectedly, however, the appropriateness of the test methodologies, the concerns of multiplicity, and the compensation methodologies have not been sufficiently confirmed.ResultsThe appropriateness was checked by means of coincidence between the methodologies' premises and the statistical characteristics of data found in two typical microarray platforms. As expected, normality was observed in within-group data differences, supporting application of t-test and F-test statistics. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of p-values were rather complex. These characteristics are inconsistent with premises underlying the compensation methodologies, which assume that most of the null hypotheses are true. The evidence also raised concerns about multiplicity. In transcriptomic studies, FWER should not be critical, as analyses at higher levels would not be influenced by a few false positives. Additionally, the concerns for FDR are not suitable for the sharp null hypotheses on expression levels.ConclusionsTherefore, although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly.

Dataset Information

Integrating personalized medical test contents with XML and XSL-FO.

Publications

Integrating personalized medical test contents with XML and XSL-FO.

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