Evidence map›Paper›PMID 39664510›Full record

ArticleCampbell systematic reviews2024

Protocol: Machine learning for selecting moderators in meta-analysis: A systematic review of methods and their applications, and an evaluation using data on tutoring interventions.

Jens Dietrichson, Rasmus Klokker, Trine Filges, Elizabeth Bengtsen, Therese D Pigott

Abstract read
In one paragraph

Article in Campbell systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Jens DietrichsonQuantitative Methods, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Rasmus KlokkerQuantitative Methods, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Trine FilgesQuantitative Methods, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Elizabeth BengtsenAdministration, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Therese D PigottSchool of Public Health Georgia State University Atlanta Georgia USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives This is the protocol for a Campbell systematic review. The objectives are as follows: The first objective is to find and describe machine and statistical learning (ML) methods designed for moderator meta-analysis. The second objective is to find and describe applications of such ML methods in moderator meta-analyses of health, medical, and social science interventions. These two parts of the meta-review will primarily involve a systematic review and will be conducted according to guidelines specified by the Campbell Collaboration (MECCIR guidelines). The outcomes will be a list of ML methods that are designed for moderator meta-analysis (first objective), and a description of how (some of) these methods have been applied in the health, medical, and social sciences (second objective). The third objective is to examine how the ML methods identified in the meta-review can help researchers formulate new hypotheses or select among existing ones, and compare the identified methods to one another and to regular meta-regression methods for moderator analysis. To compare the performance of different moderator meta-analysis methods, we will apply the methods to data on tutoring interventions from two systematic reviews of interventions to improve academic achievement for students with or at risk-of academic difficulties, and to an independent test sample of tutoring studies published after the search period in the two reviews.

Indexed as

machine learningmoderator analysistutoringvariable selection

Identifiers

PMID39664510
PMCPMC11632158

What OpenQuestion holds

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Registered trials

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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.