Evidence map›Paper›PMID 33349237›Full record

ArticleBMC bioinformatics2020

A2A: a platform for research in biomedical literature search.

Maciej Rybinski, Sarvnaz Karimi, Vincent Nguyen, Cecile Paris

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Maciej RybinskiCSIRO Data61, Sydney, Australia. maciek.rybinski@csiro.au.ORCID http://orcid.org/0000-0002-0174-0567
Sarvnaz KarimiCSIRO Data61, Sydney, Australia.
Vincent NguyenCSIRO Data61, Sydney, Australia.
Cecile ParisCSIRO Data61, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFinding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles-or as we call them documents-by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine.

resultsTo provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents.

conclusionsWe introduce the A2A search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.

Indexed as

SoftwareBiomedical ResearchDatabases, FactualHumansInformation Storage and RetrievalMedical Subject HeadingsBiomedical searchClinical decision supportEvaluationGenomicsInformation retrievalPrecision medicineSoftware platform

Identifiers

PMID33349237
PMCPMC7751125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.