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Alexithymia, anxiety, and depression in patients with vitiligo

2021· article· en· W3174291754 on OpenAlexaboutno aff
Nazlı Dizen Namdar, Yasemin Kurtoglu

Bibliographic record

VenueAnnals of Medical Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsnot available
Fundersnot available
KeywordsVitiligoAlexithymiaAnxietyToronto Alexithymia ScaleBeck Depression InventoryDepression (economics)PsychosocialBeck Anxiety InventoryClinical psychologyMedicinePsychologyPsychiatryDermatology

Abstract

fetched live from OpenAlex

Aim: Characterized by hypopigmented cutaneous lesions, vitiligo is a chronic dermatological disease with psychosocial effects. The goal of our study is to search alexithymia, anxiety, and depression levels in vitiligo patients.Materials and Methods: In total, 50 vitiligo patients aged over 18 years and 70 healthy control subjects compatible with age and gender were evaluated. All participants filled in the questionnaires of the Toronto Alexithymia Scale (TAS-20), Beck Anxiety inventory (BAI), and Beck Depression inventory (BDI).Results: In vitiligo patients, depression and anxiety levels were meaningful higher than the controls (p = 0.009, p = 0.000, respectively), but there was no meaningful difference between alexithymia scores (p = 0.103). There was no correlation between psychiatric scale scores and disease duration, age, gender, and education levels in vitiligo patients. A positive correlation was determined between alexithymia levels and anxiety and depression levels in vitiligo.Conclusion: In this study, anxiety and depression levels were found to be high in patients with vitiligo. According to the results of our study, vitiligo should not only be evaluated dermatologically but also psychologically. In this way, we think that patients' compliance with treatment and treatment success will increase.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

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

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 head0.053
GPT teacher head0.407
Teacher spread0.354 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2021
Admission routes1
Has abstractyes

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