The effect of psychopathology on quality of life and disability in patients with fibromyalgia
Bibliographic record
Abstract
Objectives: The aim of the study was to investigate the relationship between pain, depression, anxiety, somatic amplification and alexithymia in patients with fibromyalgia syndrome (FMS), and on quality of life and disability. As a secondary goal, the predictors of disability were evaluated. Methods: Participants were 112 female patients aged 18 and over, applied to the outpatient clinic of University of Health Sciences Bursa Yüksek İhtisas Training and Research Hospital Medical Ecology and Hydroclimatology department and diagnosed with FMS according to ACR 2016 Revised Fibromyalgia Diagnosis Criteria. The Sociodemographic Data Form, Visual Analog Scale (VAS), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Toronto Alexithymia Scale (TAS-20), Somatosensory Amplification Scale (SSAS), Fibromyalgia Impact Questionnaire (FIQ) and Health Survey Questionnaire Short Form (SF-36) were applied to each participant. All data were analyzed with correlation and linear regression. Results: Increased pain intensity, depression, anxiety, somatic amplification, "difficulty identifying feeling" and "difficulty describing feelings" dimensions of alexithymia were found related to lower quality of life and increased disability. Depression, somatic amplification, and pain severity were defined as the predictors of disability in FMS. Conclusions: Psychiatric examination of FMS patients especially in terms of depression, anxiety, alexithymia and somatic amplification as well as their physical complaints can be beneficial to minimize disability and increase the quality of life. To our best knowledge, this is the first study to show somatic amplification as a predictor of disability in FMS patients. Further studies will be helpful to understand this relationship.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".