Evidence map›Paper›PMID 21346931›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010

Automatically classifying the role of citations in biomedical articles.

Shashank Agarwal, Lisha Choubey, Hong Yu

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Citation Sentiment Analysis in Clinical Trial Papers.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015
    Article
  8. Article
  9. The biomedical discourse relation bank.BMC bioinformatics · 2011
    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

3 authors.

Shashank AgarwalUniversity of Wisconsin-Milwaukee, Milwaukee, WI.
Lisha Choubey
Hong Yu

Funding

HERMES - Help physicians to Extract and aRticulate Multimedia information from liR01LM009836 · NLM · UNIVERSITY OF WISCONSIN MILWAUKEE · PI YU, HONG · 2007 to 2009
$1.3M
NLM NIH HHS R01 LM009836
6 · The paper itself

Abstract

Citations are widely used in scientific literature. The traditional model of referencing considers all citations to be the same; however, semantically, citations play different roles. By studying the context in which citations appear, it is possible to determine the role that they play. Here, we report on the development of an eight-category classification scheme, annotation using that scheme, and development and evaluation of supervised machine-learning classifiers using the annotated data. We annotated 1,710 sentences using the annotation schema and our trained classifier obtained an average F1-score of 76.5%. The classifier is available for free as a Java API from http://citation.askhermes.org.

Indexed as

Artificial IntelligenceSupervised Machine LearningAlgorithmsHumans

Identifiers

PMID21346931
PMCPMC3041379

What OpenQuestion holds

Textmetadata
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.