PubMed · 15131928
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P Mark Logan. 2004. Preparing an educational exhibit.. https://pubmed.ncbi.nlm.nih.gov/15131928/
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AIMS: The quality of randomised controlled trials (RCTs) is associated with bias. Thus, reports of RCTs must have enough detail of key elements of quality to enable them to be interpreted properly. This study examines the quality of abstracts of RCTs reported at the ICS meeting in Heidelberg in 2002, using the CONSORT statement as the gold standard. MATERIALS AND METHODS: All of the abstracts accepted for the meeting at Heidelberg were read to identify reports of RCTs. Copies of these were printed and examined to see whether they complied with the 22 items in the CONSORT statement. As these were all abstracts the first CONSORT item was changed so that to comply the title had to say it was a randomised trial. Each item was scored as not met, partially met, met. RESULTS: Fifty-three reports of RCTs were found. Five of these were podium presentations, 14 discussion posters, and 34 non-discussion posters. Most reports did not comply with many of the items in the CONSORT statement, lacking particularly in technical details of the methods (only one study clearly reported hidden allocation to groups), and how the results were presented (only two studies fully reported results). Only 2/53 of the abstracts complied fully with more than 10 of the items, and 30/53 did not comply at all with 10 or more. CONCLUSIONS: The quality of reporting of studies at ICS is so poor that it is difficult to interpret the results. Reporting was particularly poor on the details of the randomisation and the numeric results.
MOTIVATION: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing. RESULTS: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes.
UNLABELLED: MEDLINE/PubMed is one of the most important information sources for bioinformatics text mining. However, there remain limitations in working with MEDLINE/PubMed citations. For example, PubMed imposes an upper limit of 10,000 for downloading PMID list or citations; and MEDLINE files are too large for most off-the-shelf XML parsers. We developed a Java package, MedKit, to work-around the limitations, as well as provide other useful functionalities, e.g. random sampling. Its four modules (querier, sampler, fetcher and parser) can work independently, or be pipelined in various combinations. It can be used as a stand-alone GUI application, or integrated into other text-mining systems. Text mining researchers and others may download and use the toolkit free for non-commercial purposes. AVAILABILITY: http://metnetdb.gdcb.iastate.edu/medkit CONTACT: berleant@iastate.edu.