Evidence map›Paper›PMID 42458133›Full record

ArticleBehavior research methods2026

Complex dynamics in psychological data: Mapping individual symptom trajectories to group-level patterns.

Eleonora Vitanza, Pietro De Lellis, Chiara Mocenni, Manuel Ruiz Marin

Abstract read
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In one paragraph

Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Eleonora VitanzaDepartment of Information Engineering and Mathematics, University of Siena, Via Roma 56, Siena, 53100, Italy. e.vitanza@phd.poliba.it.ORCID http://orcid.org/0000-0002-4229-3089
Pietro De LellisDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy.
Chiara MocenniDepartment of Information Engineering and Mathematics, University of Siena, Via Roma 56, Siena, 53100, Italy.
Manuel Ruiz MarinDepartment of Quantitative Methods, Law and Modern Languages, Universidad Politécnica de Cartagena, Calle Real 3, Edificio CIM, 30201, Cartagena, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study integrates causal inference, graph analysis, temporal complexity measures, and machine learning to examine whether individual symptom trajectories can reveal meaningful diagnostic patterns. Testing on a longitudinal dataset of

Indexed as

Generalized Anxiety DisorderMajor Depressive DisorderAdultAlgorithmsFemaleHumansLongitudinal StudiesMachine LearningMaleCausal inferenceComplexityGroup comparisonLongitudinal dataMachine learningNetwork analysisPsychopathologyTime series classification

Identifiers

PMID42458133

What OpenQuestion holds

Textmetadata
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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.