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Record W3149284498 · doi:10.1093/rheumatology/keab074

Comment on: Association between depression and anxiety with skin and musculoskeletal clinical phenotypes in systemic lupus erythematosus: reply

2021· letter· en· W3149284498 on OpenAlexafffund
Kathleen Bingham, Zahi Touma

Bibliographic record

VenueLara D. Veeken · 2021
Typeletter
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsToronto Western HospitalUniversity of TorontoToronto General HospitalUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of TorontoArthritis SocietyLupus Research Alliance
KeywordsMedicineAssociation (psychology)AnxietyDepression (economics)Systemic lupusSystemic lupus erythematosusDermatologyPsychiatryInternal medicinePsychotherapistDisease

Abstract

fetched live from OpenAlex

Dear Editor, We thank Dr Tecer and colleagues for their interest in and commentary on our article [1], ‘Association between depression and anxiety with skin and musculoskeletal clinical phenotypes in systemic lupus erythematosus’. We agree with the authors’ point that developing an understanding of the epidemiology, risk factors and pathophysiology of depression and anxiety in patients with SLE is a critical step in optimizing care. Tecer et al. bring up several important points regarding the potential contributions of autoantibodies, medications (glucocorticoids and opioids), and chronic comorbidities and habits to depression and anxiety in SLE. We agree that these factors likely play a role in the pathophysiology of depression and anxiety for many patients. In addition to the factors identified in our article and by Tecer et al., a variety of psychosocial, clinical and sociodemographic factors have been found to be associated with depression or anxiety in SLE in observational studies, including fatigue severity [2], disease activity and active disease [3, 4], cognitive impairment [5] marital relationship quality [2], psychological reserve [6] and financial strain [7]. The variety of findings in this literature reflects differing study designs, populations and methodologies and also reflects the heterogeneity of depression and anxiety in SLE. Depression and anxiety symptoms are common in the general population and may or may not be attributable to neuropsychiatric symptoms of SLE [8]. Given the variety of different potential contributors to depression and anxiety in SLE, we did not intend to build an explanatory model incorporating all potential variables suggested as being found in the literature, as our sample size was not adequate for that type of analysis. Instead, we aimed to perform a more focussed study, evaluating easily accessible and patient data that could be measured as part of routine care. We did not exclude patients using antidepressant medication or with a history of depression and anxiety, as we were interested in capturing clinically significant anxiety or depression (as defined by validated cut-offs) whether or not patients had been formally diagnosed or treated for a depressive or anxiety disorder. In our cohort, fibromyalgia is diagnosed based on ACR 1990 classification criteria, which involves the presence of chronic (>3 months) widespread pain as well as the need for at least 11 out of 18 possible tender points [9]. We also agree with Dr Tecer and colleagues on their comment regarding the importance of antiphospholipid antibodies and their association with different neuropsychiatric syndromes (NPSLE). Nevertheless, we and others have shown different NPSLE syndromes often occur regardless of the status of different antibodies, including antiphospholipid [10]. Our study aims were as follows: (i) to replicate other studies on the prevalence of depression and anxiety in our SLE cohort using measures and cut-offs with evidence in SLE; and (ii) to explore the relationship between clinical phenotypes of SLE and common sociodemographic factors with depression and anxiety in SLE. We hope that our results regarding the prevalence of anxiety (34%) and depression (27%) in our cohort will add to the literature regarding the burden of mood and anxiety symptoms in SLE. We also hope that our findings that SLE patients with musculoskeletal and skin involvement, shorter disease duration, history of fibromyalgia and unemployment are more likely to exhibit depression or anxiety will be helpful in informing future larger prospective studies in this area. Ultimately, convergent evidence from large cohort studies, intervention trials and basic science and translational research will allow us to gain a deeper understanding of anxiety and depression in SLE. Z.T. is supported by funding from the Arthritis Society, the Canadian Institutes of Health Research, Lupus Research Alliance, and the Department of Medicine, University of Toronto. Z.T.’s laboratory is also supported by donations from the Kathi and Peter Kaiser family, the Lou and Marissa Rocca family and the Bozzo family. K.B. is supported by funding from the University of Toronto Department of Psychiatry’s Excellence Fund and the Labatt Family Network’s Catalyst Fund. Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. Disclosure statement: The authors have declared no conflicts of interest.

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)0.006
metaresearch head score (Gemma)0.033
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.062
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0620.046
Insufficient payload (model declined to judge)0.0080.009

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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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
GenreCommentary

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".

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Citations1
Published2021
Admission routes2
Has abstractno

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