Observational studyBMC psychiatry2020
Clinical judgement, case complexity and symptom scores as predictors of outcome in depression: an exploratory analysis.
Observational study in BMC psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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
3 citing papers in PubMed, 9 citations in OpenAlex.
- Data leakage in machine learning studies creep into meta-analytic estimates of predictive performance.Molecular psychiatry · 2025Article
- Association between physical multimorbidity and common mental health disorders in rural and urban Malawian settings: Preliminary findings from Healthy Lives Malawi long-term conditions survey.PLOS global public health · 2024Article
- Helping reviewers assess statistical analysis: A case study from analytic methods.Analytical science advances · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical guidelines for depression in adults recommend the use of outcome measures and stepped care models in routine care. Such measures are based on symptom severity, but response to treatment is likely to also be influenced by personal and contextual factors. This observational study of a routine clinical sample sought to examine the extent to which "symptom severity measures" and "complexity measures" assess different aspects of patient experience, and how they might relate to clinical outcomes, including disengagement from treatment.
methodsSubjects with symptoms of depression (with or without comorbid anxiety) were recruited from people referred to an established Primary Care Mental Health Team using a stepped care model. Each participant completed three baseline symptom measures (the Personal Health Questionnaire (PHQ), Generalised Anxiety Disorder questionnaire (GAD) and Clinical Outcomes in Routine Evaluation (CORE-10)), and two assessments of "case complexity" (the Minnesota-Edinburgh Complexity Assessment Measure (MECAM) and a local complexity assessment). Clinician perception of likely completion of treatment and patient recovery was also assessed. Outcome measures were drop out and clinical improvement on the PHQ.
results298 subjects were recruited to the study, of whom 258 had a sufficient dataset available for analysis. Data showed that the three measures of symptom severity used in this study (PHQ, GAD and CORE-10) seemed to be measuring distinct characteristics from those associated with the measures of case complexity (MECAM, previous and current problem count). Higher symptom severity scores were correlated with improved outcomes at the end of treatment, but there was no association between outcome and complexity measures. Clinicians could predict participant drop-out from care with some accuracy, but had no ability to predict outcome from treatment.
conclusionsThese results highlight the extent to which drop-out complicates recovery from depression with or without anxiety in real-world settings, and the need to consider other factors beyond symptom severity in planning care. The findings are discussed in relation to a growing body of literature investigating prognostic indicators in the context of models of collaborative care for depression.
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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.