Fibromyaljisi Olan Kadın Hastalarda Mental Belirtiler Yaşam Kalitesi ve Hastalık Şiddeti ile İlişkilidir / Mental Symptoms Are Related With Impact Of The Disease And Impairment In Quality Of Life In Female Patients With Fibromyalgia
Bibliographic record
Abstract
Mental symptoms are related with impact of the disease and impairment in quality of life in female patients with fibromyalgiaObjective: Diagnostic criteria of fibromyalgia are revised in 2010 by including mental symptoms and excluding tender points.Although dominance of pain still prevails on the diagnostic criteria, quality of life (QoL) surveys showed that fibromyalgia is strongly associated with mental components of health status, i.e. depression, anxiety and alexithymia.It is aimed to assess determinants of QoL and impact of the disease in patients with fibromyalgia.Methods: Fifty seven female outpatients (mean age±SD: 40.93±6.85;age range: 24-56) with fibromyalgia were enrolled.Fibromyalgia Impact Questionnaire, Short Form-36 QoL survey, Beck Depression Inventory, State-Trait Anxiety Inventory, 20-item Toronto Alexithymia Scale were the measurement tools.Results: Predictor of impact of the disease was alexithymia, particularly, difficulty in identifying feelings (DIF) domain.Predictor of physical health was age, whereas predictors of mental health were depression and trait anxiety.Discussion: Alexithymia, particularly DIF domain may be a more specific predictor of fibromyalgia symptoms, whereas depression and anxiety are more burdensome mental symptoms for fibromyalgia.Pain and mental symptoms are seemed to be processed separately.Targeting mental symptoms may provide better treatment outcomes, thus multidisciplinary approaches including psychiatry are necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".