Evidence map›Paper›PMID 37705060›Full record

ArticleSystematic reviews2023

Increasing the efficiency of study selection for systematic reviews using prioritization tools and a single-screening approach.

Siw Waffenschmidt, Wiebke Sieben, Thomas Jakubeit, Marco Knelangen, Inga Overesch, Stefanie Bühn, Dawid Pieper, Nicole Skoetz, Elke Hausner

Abstract read
In one paragraph

Article in Systematic reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Digital Tools to Support the Systematic Review Process: An Introduction.Journal of evaluation in clinical practice · 2025
    Review
  11. Article
  12. Evidence-based research.Systematic reviews · 2024
    Article
  13. 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

9 authors.

Siw WaffenschmidtInstitute for Quality and Efficiency in Health Care, Cologne, Germany. siw.waffenschmidt@iqwig.de.ORCID http://orcid.org/0000-0001-6860-6699
Wiebke SiebenInstitute for Quality and Efficiency in Health Care, Cologne, Germany.
Thomas JakubeitInstitute for Quality and Efficiency in Health Care, Cologne, Germany.
Marco KnelangenInstitute for Quality and Efficiency in Health Care, Cologne, Germany.
Inga OvereschInstitute for Quality and Efficiency in Health Care, Cologne, Germany.
Stefanie BühnInstitute for Research in Operative Medicine, Herdecke University, Witten, Germany.
Dawid PieperInstitute for Research in Operative Medicine, Herdecke University, Witten, Germany.
Nicole SkoetzEvidence-Based Medicine, Department I of Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Elke HausnerInstitute for Quality and Efficiency in Health Care, Cologne, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystematic literature screening is a key component in systematic reviews. However, this approach is resource intensive as generally two persons independently of each other (double screening) screen a vast number of search results. To develop approaches for increasing efficiency, we tested the use of text mining to prioritize search results as well as the involvement of only one person (single screening) in the study selection process.

methodOur study is based on health technology assessments (HTAs) of drug and non-drug interventions. Using a sample size calculation, we consecutively included 11 searches resulting in 33 study selection processes. Of the three screeners for each search, two used screening tools with prioritization (Rayyan, EPPI Reviewer) and one a tool without prioritization. For each prioritization tool, we investigated the proportion of citations classified as relevant at three cut-offs or STOP criteria (after screening 25%, 50% and 75% of the citation set). For each STOP criterion, we measured sensitivity (number of correctly identified relevant studies divided by the total number of relevant studies in the study pool). In addition, we determined the number of relevant studies identified per single screening round and investigated whether missed studies were relevant to the HTA conclusion.

resultsOverall, EPPI Reviewer performed better than Rayyan and identified the vast majority (88%, Rayyan 66%) of relevant citations after screening half of the citation set. As long as additional information sources were screened, it was sufficient to apply a single-screening approach to identify all studies relevant to the HTA conclusion. Although many relevant publications (n = 63) and studies (n = 29) were incorrectly excluded, ultimately only 5 studies could not be identified at all in 2 of the 11 searches (1x 1 study, 1x 4 studies). However, their omission did not change the overall conclusion in any HTA.

conclusionsEPPI Reviewer helped to identify relevant citations earlier in the screening process than Rayyan. Single screening would have been sufficient to identify all studies relevant to the HTA conclusion. However, this requires screening of further information sources. It also needs to be considered that the credibility of an HTA may be questioned if studies are missing, even if they are not relevant to the HTA conclusion.

Indexed as

Data MiningInformation SourcesHumansSystematic Reviews as TopicTechnology Assessment, BiomedicalMethodologyStudy selectionSystematic reviews

Identifiers

PMID37705060
PMCPMC10500815

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.