Evidence map›Paper›PMID 38037069›Full record

ArticleBMC psychiatry2023

Methods for quantifying the heterogeneity of psychopathology.

John F Buss, Ashley L Watts, Lorenzo Lorenzo-Luaces

Abstract read
In one paragraph

Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

2 citing papers in PubMed.

  1. Article
  2. Review
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

3 authors.

John F BussDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. bussj@iu.edu.
Ashley L WattsDepartment of Psychology, Vanderbilt University, Nashville, TN 37420, USA.
Lorenzo Lorenzo-LuacesDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

Funding

Training in Clinical Translational Science: Maximizing the Public Health ImpactT32MH103213 · NIMH · TRUSTEES OF INDIANA UNIVERSITY · PI WILLIAM P HETRICK · 2015 to 2026
$3.4M
Resolving sources of heterogeneity and comorbidity in alcohol use disorderR00AA028306 · NIAAA · VANDERBILT UNIVERSITY · PI WATTS, ASHLEY L. · 2023 to 2025
$745k
NIAAA NIH HHS R00 AA028306NIMH NIH HHS T32 MH103213
6 · The paper itself

Abstract

objectivesSpecifiers for a major depressive disorder (MDE) are supposed to reduce diagnostic heterogeneity. However, recent literature challenges the idea that the atypical and melancholic specifiers identify more homogenous or coherent subgroups. We introduce the usage of distance metrics to characterize symptom heterogeneity. We attempt to replicate prior findings and explore whether symptom heterogeneity is reduced using specifier subgroups.

methodsWe used data derived from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC Wave I; N = 5,749) and the Sequenced Treatment Alternatives to Relieve Depression study (STAR*D; N = 2,498). We computed Hamming and Manhattan distances from study participants' unique symptom profiles. Distances were standardized from 0-1 and compared by their within- and between-group similarities to their non-specifier counterparts for the melancholic and atypical specifiers.

resultsThere was no evidence of statistically significant differences in heterogeneity for specifier (i.e., melancholic or atypical) vs. non-specifier designations (i.e., non-melancholic vs. non-atypical).

conclusionReplicating prior work, melancholic and atypical depression specifiers appear to have limited utility in reducing heterogeneity. The current study does not support the claim that specifiers create more coherent subgroups as operationalized by similarity in the number of symptoms and their severity. Distance metrics are useful for quantifying symptom heterogeneity.

Indexed as

Major Depressive DisorderDepressionDiagnostic and Statistical Manual of Mental DisordersHumansPsychopathologyAtypicalClassificationDepressionMelancholia

Identifiers

PMID38037069
PMCPMC10690966

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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.