Evidence map›Paper›PMID 32525207›Full record

ArticleDatabase : the journal of biological databases and curation2020

Identifying main finding sentences in clinical case reports.

Mengqi Luo, Aaron M Cohen, Sidharth Addepalli, Neil R Smalheiser

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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.

Mengqi LuoDepartment of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.
Aaron M CohenDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA.
Sidharth AddepalliDepartment of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.
Neil R SmalheiserDepartment of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.

Funding

Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based MedicineR01LM010817 · NLM · UNIVERSITY OF ILLINOIS AT CHICAGO · PI COHEN, AARON M., SMALHEISER, NEIL R · 2010 to 2019
$4.5M
NLM NIH HHS R01 LM010817
6 · The paper itself

Abstract

Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0.

Indexed as

Machine LearningData MiningHumansMedical RecordsNatural Language ProcessingSoftware

Identifiers

PMID32525207
PMCPMC7287507

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.