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PubMed2000

Questioning validity in the area of ergonomics/human factors.

This paper focuses on the analysis of deviation in findings within ergonomics/human factors (E/HF) research. The current terms used to address the analysis of deviation in empirical research revolve around the notion of validity. In E/HF research papers, 'validity' is widely interpreted and includes its common parlance usage. More importantly, analysis frequently limits 'validation' to the equivalent of 'verification', eventually resulting in 'validity' as a label of little significance. To clarify the analysis of deviation, 'investigative syntaxes' are introduced to show what exactly should or can be questioned when deviation is observed, i.e. either empirical findings or propositions, and how this questioning can be structured. The possibility is discussed that, with or without the help of these syntaxes, validation may become a method of inquiry; a productive means of generating significant theoretical questions which bear directly on empirical work.

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PubMed2006

A systematic review of systematic reviews and meta-analyses of animal experiments with guidelines for reporting.

To maximize the findings of animal experiments to inform likely health effects in humans, a thorough review and evaluation of the animal evidence is required. Systematic reviews and, where appropriate, meta-analyses have great potential in facilitating such an evaluation, making efficient use of the animal evidence while minimizing possible sources of bias. The extent to which systematic review and meta-analysis methods have been applied to evaluate animal experiments to inform human health is unknown. Using systematic review methods, we examine the extent and quality of systematic reviews and meta-analyses of in vivo animal experiments carried out to inform human health. We identified 103 articles meeting the inclusion criteria: 57 reported a systematic review, 29 a systematic review and a meta-analysis, and 17 reported a meta-analysis only. The use of these methods to evaluate animal evidence has increased over time. Although the reporting of systematic reviews is of adequate quality, the reporting of meta-analyses is poor. The inadequate reporting of meta-analyses observed here leads to questions on whether the most appropriate methods were used to maximize the use of the animal evidence to inform policy or decision-making. We recommend that guidelines proposed here be used to help improve the reporting of systematic reviews and meta-analyses of animal experiments. Further consideration of the use and methodological quality and reporting of such studies is needed.

Animal Experimentation↗
PubMed2002

Concepts of disablement in documents guiding physical therapy practice.

PURPOSE: To describe disability concepts used within documents guiding physical therapist practice. METHODS: Content analysis was performed on the Guide to Physical Therapist Practice, second edition; A Normative Model of Physical Therapist Professional Education, version 2000; APTA House of Delegates Standards, Policies, Positions and Guidelines; APTA Board of Directors Policies, Positions and Guidelines; The Model Practice Act for Physical Therapy and The Illinois Physical Therapy Act. Using a word sense framework, text-in-context lists were compiled, from which contextual themes and counts for all occurrences of disability terms were developed. RESULTS: Across documents, contextual themes with number of occurrences were: disability as role performance limitations within specific contexts: 819; disability resulting from health status: 39, disability law: 29; and rights of individuals with disabilities: 25. CONCLUSIONS: Documents guiding physical therapist practice commonly conceptualize disability as individual limitations within specific contexts and infrequently conceptualize disability as a societal phenomenon affecting persons across most settings and circumstances. It is believed that a concept of disability that is more inclusive of broad, as well as specific, contexts of disability may lead to improved physical therapy management for individuals with a wide range of performance capacities.

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PubMedDate not supplied

Use of information-seeking strategies for developing systematic reviews and engaging in evidence-based practice: the application of traditional and comprehensive Pearl Growing. A review.

BACKGROUND: Efficient library searches for research evidence are critical to practitioners who wish to engage in evidence-based practice (EBP) as well as researchers who seek to develop systematic reviews. AIMS: This review will propose the benefits of the search technique 'Pearl Growing' ('Traditional Pearl Growing') as well as an adaptation of this technique ('Comprehensive Pearl Growing'), until now ignored by the literature on EBP and systematic reviews, to aid in the retrieval of research evidence. These search techniques are illustrated with examples from the field of augmentative and alternative communication. MAIN CONTRIBUTIONS: Traditional Pearl Growing is proposed as an important addition to the arsenal of EBP search strategies for practitioners. The literature on Traditional Pearl Growing is extended in that EBP presents a newly identified purpose for this technique and the benefits in identifying appropriate quality filter goes beyond its previously exclusive focus on keywords. Comprehensive Pearl Growing is projected as a new strategy for researchers searching for studies to be included in systematic reviews. Not only does it provide data-based guidance in selecting effective keywords and quality filters, but also it provides appropriate databases. CONCLUSIONS: Although the techniques Traditional Pearl Growing and Comprehensive Pearl Growing are believed to be useful for locating research evidence in any field, it may be particularly important for interdisciplinary topics where the use of effective controlled vocabulary plays a greater role in bringing together evidence that may be scattered across databases.

Communication Disorders↗
PubMed1997

Predicting look-alike and sound-alike medication errors.

A model for predicting medication name confusion is described. Many medication errors are caused by look-alike and sound-alike medication names, yet few procedures exist to ensure the safety of new drug nomenclature or to identify confusingly similar names from within existing databases. In this study, three automated, quantitative measures of orthographic similarity (i.e., similarity in spelling) were identified (bigram similarity, trigram similarity, and Levenshtein distance). The relationship between orthographic similarity and the likelihood of a medication error was examined. For each measure of similarity, the frequency distribution of similarity scores for pairs of drug names previously reported to cause confusion (error pairs) was compared with the distribution of similarity scores for control pairs randomly selected from the general index of USP DI-Volume I: Drug Information for the Health Care Professional. Then, three parallel, unmatched case-control studies were conducted to discover whether similarity was a significant risk factor for medication errors. Finally, on the basis of the three similarity measures, tests for predicting confusion were developed and evaluated. For each similarity measure, the frequency distribution of error pairs was significantly different from that for control pairs, and orthographic similarity was a significant risk factor for medication errors. Pairs of names whose measures of similarity exceeded present thresholds were between 25 and 523 times more likely to be involved in a medication error than pairs whose similarity did not exceed these thresholds. A prognostic test that correctly identified 91% of all pairs as either errors or controls was developed. This test had a sensitivity of 84% and a specificity of 99%. Automated measures of similarities between medication names can form the basis of highly accurate, sensitive, and specific tests of the potential for errors with look-alike and sound-alike medication names.

Humans↗
PubMed2005

Hairpins in bookstacks: information retrieval from biomedical text.

Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.

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PubMed2005

Online tools to support literature-based discovery in the life sciences.

In biomedical research, the amount of experimental data and published scientific information is overwhelming and ever increasing, which may inhibit rather than stimulate scientific progress. Not only are text-mining and information extraction tools needed to render the biomedical literature accessible but the results of these tools can also assist researchers in the formulation and evaluation of novel hypotheses. This requires an additional set of technological approaches that are defined here as literature-based discovery (LBD) tools. Recently, several LBD tools have been developed for this purpose and a few well-motivated, specific and directly testable hypotheses have been published, some of which have even been validated experimentally. This paper presents an overview of recent LBD research and discusses methodology, results and online tools that are available to the scientific community.

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PubMed2005

What makes a gene name? Named entity recognition in the biomedical literature.

The recognition of biomedical concepts in natural text (named entity recognition, NER) is a key technology for automatic or semi-automatic analysis of textual resources. Precise NER tools are a prerequisite for many applications working on text, such as information retrieval, information extraction or document classification. Over the past years, the problem has achieved considerable attention in the bioinformatics community and experience has shown that NER in the life sciences is a rather difficult problem. Several systems and algorithms have been devised and implemented. In this paper, the problems and resources in NER research are described, the principal algorithms underlying most systems sketched, and the current state-of-the-art in the field surveyed.

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PubMed2003

GENIA corpus--semantically annotated corpus for bio-textmining.

MOTIVATION: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. RESULTS: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.

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PubMed2003

Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup.

MOTIVATION: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful. RESULTS: We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new ('blind') articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.

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PubMed2003

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation.

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.

Artificial Intelligence↗
PubMed2004

Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network.

MOTIVATION: There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature. RESULTS: We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO_relationships.htm

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PubMed2004

Distribution of information in biomedical abstracts and full-text publications.

MOTIVATION: Full-text documents potentially hold more information than their abstracts, but require more resources for processing. We investigated the added value of full text over abstracts in terms of information content and occurrences of gene symbol--gene name combinations that can resolve gene-symbol ambiguity. RESULTS: We analyzed a set of 3902 biomedical full-text articles. Different keyword measures indicate that information density is highest in abstracts, but that the information coverage in full texts is much greater than in abstracts. Analysis of five different standard sections of articles shows that the highest information coverage is located in the results section. Still, 30-40% of the information mentioned in each section is unique to that section. Only 30% of the gene symbols in the abstract are accompanied by their corresponding names, and a further 8% of the gene names are found in the full text. In the full text, only 18% of the gene symbols are accompanied by their gene names.

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