Data extraction methods: an analysis of internal reporting discrepancies in single manuscripts and practical advice.

Clicks: 424
ID: 56957
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #9 of 75 articles by views in journal of clinical epidemiology

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Data extraction from reports about experimental or observational studies is a crucial methodological step informing evidence syntheses, such as systematic reviews (SRs) and overviews of SRs. These discrepancies were defined as pairs of statements that could not both be true. Authors of SRs and overviews of SRs can encounter reporting discrepancies among multiple sources when extracting data - a manuscript and a conference abstract, a manuscript and a clinical trial registry. However, these discrepancies can also be found within a single manuscript published in a scientific journal. Hereby we describe examples of internal reporting discrepancies that can be found in a single source, with the aim of raising awareness among authors of SRs and overviews of SRs about such potential methodological issues. Authors of SRs and overviews of SRs should check whether the same information is reported in multiple places within a study, and compare that information. Independent data extraction by two reviewers increases the chance of finding discrepancies, if they exist. We provide advice on how to deal with different types of discordances and how to report such discordances when conducting SRs and overviews of SRs.
Reference Key
puljak2019datajournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Puljak, Livia;Riva, Nicoletta;Parmelli, Elena;González-Lorenzo, Marien;Moja, Lorenzo;Pieper, Dawid;
Journal journal of clinical epidemiology
Year 2019
DOI
S0895-4356(19)30222-7
URL
Keywords
data systematic reviews errors reporting data extraction discrepancies

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.