ArticleFrontiers in psychiatry2022
Using machine learning to investigate earning capacity in patients undergoing psychosomatic rehabilitation-A retrospective health data analysis.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Behavioral activation for depression in groups embedded in psychosomatic rehabilitation inpatient treatment: a quasi-randomized controlled study.Frontiers in psychiatry · 2024Article
- Epistemic Trust Is a Critical Success Factor in Psychosomatic Rehabilitation-Results from a Naturalistic Multi-Center Observational Study.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psychiatric disorders increasingly contribute to disability and early retirement. This study was conducted to investigate whether machine learning can contribute to a better understanding and assessment of such a reduced earning capacity. It analyzed whether impaired earning capacity is reflected in missing treatment effects, and which interventions drive treatment effects during psychosomatic rehabilitation. Analyses were based on routine clinical data encompassing demographics, diagnoses, psychological questionnaires before, and after treatment, interventions, and an interdisciplinary assessment of earning capacity for
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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.