Alexithymia and attention deficit and their relationship with disease severity in fibromyalgia syndrome
Bibliographic record
Abstract
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".