ArticleClinical child and family psychology review2026
Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.
Article in Clinical child and family psychology review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Measuring Social and Role Recovery: Barbara Cornblatt and Colleagues' Global Functioning Scales and Their Role in Psychosocial Interventions for Clinical High-Risk and Early Psychosis.Schizophrenia bulletin · 2026Article
- Scope, Prospects, and Limitations of Living Systematic Reviews in Medical Literature: A Narrative Overview.Cureus · 2026Review
- Mapping What Works to Strengthen the Relational Ecology of Early Child Development: A Systematic Scoping Review.Clinical child and family psychology review · 2026Article
- Early Relational Health and its Impact on the Developing Brain: A Scoping Review.Clinical child and family psychology review · 2026Article
- A Scoping Review of Australian Aboriginal Early Relational Health Knowledge Systems.Clinical child and family psychology review · 2026Review
- Scientific Progress in Mapping the Relational Ecology of Early Child Development: A Systematic Scoping Review.Clinical child and family psychology review · 2026Article
- Preconception Predictors of Next Generation Early Relational Health: A Living Review of Prospective Cohort Studies.Clinical child and family psychology review · 2026Review
- Population Perspectives on Nurturing Relational Health from Early Life: A Systematic Review Series.Clinical child and family psychology review · 2026Article
- A Living Scoping Review of Universal Interventions for Promoting Relational Health in Childhood, Adolescence and Young Adulthood.Clinical child and family psychology review · 2026Article
- Population Approaches to Promoting Relational Health From Early Life.Clinical child and family psychology review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematic and meta-analytic reviews provide gold-standard evidence but are static and outdate quickly. Here we provide performance data on a new software platform, LitQuest, that uses artificial intelligence technologies to (1) accelerate screening of titles and abstracts from library literature searches, and (2) provide a software solution for enabling living systematic reviews by maintaining a saved AI algorithm for updated searches. Performance testing was based on LitQuest data from seven systematic reviews. LitQuest efficiency was estimated as the proportion (%) of the total yield of an initial literature search (titles/abstracts) that needed human screening prior to reaching the in-built stop threshold. LitQuest algorithm performance was measured as work saved over sampling (WSS) for a certain recall. LitQuest accuracy was estimated as the proportion of incorrectly classified papers in the rejected pool, as determined by two independent human raters. On average, around 36% of the total yield of a literature search needed to be human screened prior to reaching the stop-point. However, this ranged from 22 to 53% depending on the complexity of language structure across papers included in specific reviews. Accuracy was 99% at an interrater reliability of 95%, and 0% of titles/abstracts were incorrectly assigned. Findings suggest that LitQuest can be a cost-effective and time-efficient solution to supporting living systematic reviews, particularly for rapidly developing areas of science. Further development of LitQuest is planned, including facilitated full-text data extraction and community-of-practice access to living systematic review findings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.