Evidence map›Paper›PMID 37310333›Full record

ArticlePsychological medicine2023

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs.

Kathleen Miley, Piper Meyer-Kalos, Sisi Ma, David J Bond, Erich Kummerfeld, Sophia Vinogradov

Abstract read
In one paragraph

Article in Psychological medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  10. Power Analysis for Causal Discovery.International journal of data science and analytics · 2024
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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

6 authors.

Kathleen MileyDepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-7685-8623
Piper Meyer-KalosDepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
Sisi MaInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
David J BondDepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
Erich KummerfeldInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Sophia VinogradovDepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
Using a computational and network neuroscience framework to study pharmacological manipulations of state representation processes in early psychosisP50MH119569 · NIMH · UNIVERSITY OF MINNESOTA · PI MATTHEW V CHAFEE · 2020 to 2026
$24.5M
NRSA Training CoreTL1TR002493 · NCATS · UNIVERSITY OF MINNESOTA · PI FULKERSON, JAYNE ALLYN · 2018 to 2022
$3.1M
Cognition Trajectories in Cognitive Training and Early Intervention Treatment Programs in SchizophreniaR03MH117254 · NIMH · UNIVERSITY OF MINNESOTA · PI VINOGRADOV, SOPHIA · 2019 to 2020
$154k
A data-driven approach to identify the neural and cognitive basis of social functioning in health and first episode psychosisF31MH124278 · NIMH · UNIVERSITY OF MINNESOTA · PI MILEY, KATHLEEN · 2021 to 2021
$44k
NCATS NIH HHS TL1 TR002493NCATS NIH HHS UL1 TR000114NCATS NIH HHS UL1 TR002494NIMH NIH HHS F31 MH124278NIMH NIH HHS P50 MH119569NIMH NIH HHS R03 MH117254
6 · The paper itself

Abstract

backgroundWe aimed to identify unmet treatment needs for improving social and occupational functioning in early schizophrenia using a data-driven causal discovery analysis.

methodsDemographic, clinical, and psychosocial measures were obtained for 276 participants from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) trial at baseline and 6-months, along with measures of social and occupational functioning from the Quality of Life Scale. The Greedy Fast Causal Inference algorithm was used to learn a partial ancestral graph modeling causal relationships across baseline variables and 6-month functioning. Effect sizes were estimated using a structural equation model. Results were validated in an independent dataset (

resultsIn the data-generated model, greater baseline socio-affective capacity was a cause of greater baseline motivation [Effect size (ES) = 0.77], and motivation was a cause of greater baseline social and occupational functioning (ES = 1.5 and 0.96, respectively), which in turn were causes of their own 6-month outcomes. Six-month motivation was also identified as a cause of occupational functioning (ES = 0.92). Cognitive impairment and duration of untreated psychosis were not direct causes of functioning at either timepoint. The graph for the validation dataset was less determinate, but otherwise supported the findings.

conclusionsIn our data-generated model, baseline socio-affective capacity and motivation are the most direct causes of occupational and social functioning 6 months after entering treatment in early schizophrenia. These findings indicate that socio-affective abilities and motivation are specific high-impact treatment needs that must be addressed in order to promote optimal social and occupational recovery.

Indexed as

Cognitive DysfunctionPsychotic DisordersSchizophreniaAlgorithmsHumansQuality of Lifecausal discoveryEarly schizophreniafunctional outcomesmachine learningmotivationsocial cognition

Identifiers

PMID37310333
PMCPMC10106305

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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.