Evidence mapPaperPMID 40908962Full record

ArticleCochrane evidence synthesis and methods2025

Artificial Intelligence and Automation in Evidence Synthesis: An Investigation of Methods Employed in Cochrane, Campbell Collaboration, and Environmental Evidence Reviews.

Kristen L Scotti, Sarah Young, Melanie A Gainey, Haoyong Lan

Erratum issuedAbstract read
In one paragraph

Article in Cochrane evidence synthesis and methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Kristen L ScottiCarnegie Mellon University Libraries Carnegie Mellon University Pittsburgh Pennsylvania USA.ORCID https://orcid.org/0000-0002-9529-5213
Sarah YoungCarnegie Mellon University Libraries Carnegie Mellon University Pittsburgh Pennsylvania USA.
Melanie A GaineyCarnegie Mellon University Libraries Carnegie Mellon University Pittsburgh Pennsylvania USA.
Haoyong LanCarnegie Mellon University Libraries Carnegie Mellon University Pittsburgh Pennsylvania USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automation, including Machine Learning (ML), is increasingly being explored to reduce the time and effort involved in evidence syntheses, yet its adoption and reporting practices remain under-examined across disciplines (e.g., health sciences, education, and policy). This review assesses the use of automation, including ML-based techniques, in 2271 evidence syntheses published between 2017 and 2024 in the

Indexed as

artificial intelligenceliving reviewsmachine learningscreening automationsystematic reviews

Identifiers

PMID40908962
PMCPMC12407283

What Socratic 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 Socratic graph.