ArticleStudies in health technology and informatics2010
Combining relevance assignment with quality of the evidence to support guideline development.
Article in Studies in health technology and informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.JCO clinical cancer informatics · 2019Pooled it
- Using text mining for study identification in systematic reviews: a systematic review of current approaches.Systematic reviews · 2015Pooled it
- Supporting systematic reviews using LDA-based document representations.Systematic reviews · 2015Article
- Automatically finding relevant citations for clinical guideline development.Journal of biomedical informatics · 2015Article
- Representing and extracting lung cancer study metadata: study objective and study design.Computers in biology and medicine · 2015Article
- A new iterative method to reduce workload in systematic review process.International journal of computational biology and drug design · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Clinical practice guidelines are used to disseminate best practice to clinicians. Successful guidelines depend on literature that is both relevant to the questions posed and based on high quality research in accordance with evidence-based medicine. Meeting these standards requires extensive manual review. We describe a system that combines symbolic semantic processing with a statistical method for selecting both relevant and high quality studies. We focused on a cardiovascular risk factor guideline, and the overall performance of the system was 56% recall, 91% precision (F0.5-score 0.81). If quality of the evidence is not taken into account, performance drops to 62% recall, 79% precision (F0.5-score 0.75). We suggest that this system can potentially improve the efficiency of the literature review process in guideline development.
Indexed as
Identifiers
20841778PMC4296513What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.