Propositional and situational representations of text

Two informationally equivalent texts were constructed which described a fictitious town, emphasizing its spatial layout. In one version (Survey text), spatial information was in geographic terms, while in the other version (Route text), the equivalent information was provided in the form of directions for driving through the town. Subjects recalled these texts and verified old as well as inference statements. In Experiment I, subjects were able to recall the texts quite well, while showing little ability to use the information they had acquired to make inferences about spatial relations in the town which had not been directly stated in the text. With simpler texts, subjects in Experiment II were able to make infereces, especially when the form of the question corresponded to the version of the text they had read. It was concluded that free recall depended on the construction of a propositional textbase during comprehension, while inferences required a situation model, either in the form of a mental map or a procedural representation of the town. It could be shown that the form of the situation model depended on both the representation invited by the text and subject biases.

Full-text publication of abstracts presented at European Orthodontic Society congresses.

INTRODUCTION Empirical evidence has indicated that only a subsample of studies conducted reach full-text publication and this phenomenon has become known as publication bias. A form of publication bias is the selectively delayed full publication of conference abstracts. The objective of this article was to examine the publication status of oral abstracts and poster-presentation abstracts, included in the scientific program of the 82nd and 83rd European Orthodontic Society (EOS) congresses, held in 2006 and 2007, and to identify factors associated with full-length publication. METHODS A systematic search of PubMed and Google Scholar databases was performed in April 2013 using author names and keywords from the abstract title to locate abstract and full-article publications. Information regarding mode of presentation, type of affiliation, geographical origin, statistical results, and publication details were collected and analyzed using univariable and multivariable logistic regression. RESULTS Approximately 51 per cent of the EOS 2006 and 55 per cent of the EOS 2007 abstracts appeared in print more than 5 years post congress. A mean period of 1.32 years elapsed between conference and publication date. Mode of presentation (oral or poster), use of statistical analysis, and research subject area were significant predictors for publication success. LIMITATIONS Inherent discrepancies of abstract reporting, mainly related to presentation of preliminary results and incomplete description of methods, may be considered in analogous studies. CONCLUSIONS On average 52.2 per cent of the abstracts presented at the two EOS conferences reached full publication. Abstracts presented orally, including statistical analysis, were more likely to get published.

Digital multi-text editions from scratch to electronic performance. Transcription and collation routines transformed in a flexible database system

The poster demonstrates the preparatory steps of a digital multi-text edition that are abstracted from the experiences made in the Parzival Project, based at the University of Bern, the Berlin-Brandenburg Academy of Sciences and the University of Erlangen. This edition of Wolfram von Eschenbach’s German Grail novel, written shortly after 1200 and transmitted during several centuries in ca. hundred witnesses, has now been completed by more than a half of the textual corpus.

Mining free-text medical records for companion animal enteric syndrome surveillance.

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals.