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Record W3019766834 · doi:10.1007/s40744-020-00207-6

Depression in Psoriatic Arthritis: Dimensional Aspects and Link with Systemic Inflammation

2020· review· en· W3019766834 on OpenAlexaff
Ashish Jacob Mathew, Vinod Chandran

Bibliographic record

VenueRheumatology and Therapy · 2020
Typereview
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPsoriatic arthritisMedicineDepression (economics)AlexithymiaPopulationAnxietyQuality of life (healthcare)DiseaseAnhedoniaPsoriasisComorbidityIntensive care medicineInternal medicinePsychiatryImmunology

Abstract

fetched live from OpenAlex

Studying comorbidities in patients with psoriatic arthritis (PsA) provides a better understanding of the extended burden of the disease. Depression and anxiety are well recognized but understudied comorbidities in patients with PsA. The prevalence of depression is significantly higher in this patient population than in the general population, with far reaching consequences in terms of long-term quality of life. Over the past few years there has been an increasing interest in the link between inflammation and depression, with several novel studies being conducted. Recent evidence suggests a significant improvement of depression in PsA patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) as compared to conventional DMARDs. Depression in PsA is multidimensional, with recognized phenotypes, including cognitive disorder, alexithymia and anhedonia. The paucity of standardized, validated tools to screen these dimensional phenotypes remains an unmet need. Prevalence studies on depression in patients with PsA, mostly based on patient-reported outcomes, are only able to highlight the tip of the iceberg. A comprehensive, multi-disciplinary approach addressing the subdomains of depression is imperative for a better understanding of depression in PsA patients, as well as to find a way forward for improving their quality of life. In this scoping review, we explore existing evidence on the burden of depression in PsA patients, the link between inflammation and depression in these patients and the screening tools used to evaluate the subdomains of depression. Psoriatic arthritis (PsA) is a chronic, deforming arthritis associated with the skin condition psoriasis. A large number of patients with PsA are known to have another co-existing chronic disease, which adds to their overall disease burden and affects their quality of life. Depression is a common illness known to co-exist in about 20% of patients with PsA. Inflammation is a common factor between psoriatic arthritis and depressive disorders and is thought to play an important role in depression ocurring in these patients. Recent research in the field has revealed that different dimensions of depression, such as the inability to feel pleasure, loss of intellectual functions and difficulty identifying and expressing emotions, may contribute to the overall disease. It is important to screen for these dimensions while assessing PsA patients with depression. Most of the studies conducted to date have based the diagnosis of depression on self-reported questionnaires. In this article we describe the relation between inflammation and different dimensions of depression in patients with PsA and set out a feasible screening method for depression. A good understanding of depression in patients with PsA will be useful in designing treatment strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2020
Admission routes1
Has abstractyes

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