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Record W3035199111 · doi:10.5606/tftrd.2020.2926

Alexithymia and attention deficit and their relationship with disease severity in fibromyalgia syndrome

2020· article· en· W3035199111 on OpenAlexaboutno aff
Gülçin Elboğa

Bibliographic record

VenueTurkish Journal of Physical Medicine and Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaFibromyalgiaToronto Alexithymia ScaleDepression (economics)Fibromyalgia syndromeMedicinePhysical therapyPsychiatryPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to investigate the frequency of alexithymia and attention deficit and to evaluate their relationship with the severity of disease in patients with fibromyalgia syndrome (FMS). PATIENTS AND METHODS: A total of 101 patients (6 males, 95 females; mean age 45.0 years; range, 33 to 56 years) who were admitted to Gaziantep University, Medical Faculty, Physical Medicine and Rehabilitation Department between January 2013 and December 2013 and were diagnosed with FMS and 40 healthy volunteers (4 males, 36 females; mean age 41.5 years; range, 31 to 51 years) were enrolled in this study. The Fibromyalgia Impact Questionnaire (FIQ), Hamilton Depression Scale (HAM-D), Toronto Alexithymia Scale-26 (TAS-26), and Jasper-Goldberg Attention Deficit Test (ADT) were applied. RESULTS: The rate of alexithymia and possible alexithymia was 56.4% and 20.8% in the patients with FMS and 2.5% and 5% in the control group, respectively. The mean TAS-26 score was 60.1±11.7 in the patients with FMS. According to the HAM-D, depressive symptoms were seen in 72.0% and 2.5% of the patients with FMS and healthy controls, respectively. CONCLUSION: Our study results confirm the presence of psychiatric comorbidities in patients with FMS and clearly suggest that depression, alexithymia, and attention deficit are high and mutually correlated in FMS patients. Therefore, all patients should be meticulously evaluated for these conditions at the treatment stage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.015
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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