Data extraction methods: an analysis of internal reporting discrepancies in single manuscripts and practical advice.
Clicks: 307
ID: 56957
2019
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
65.0
/100
304 views
246 readers
Trending
AI Quality Assessment
Not analyzed
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.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (187 words).
Try re-searching for a better abstract.
| 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 |
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.