Alexithymia in ankylosing spondylitis
Bibliographic record
Abstract
OBJECTIVES: This study aims to determine the effect of ankylosing spondylitis (AS) on alexithymia. PATIENTS AND METHODS: In this study, a total of 55 AS patients (30 males, 25 females; mean age: 40±8 years; range, 21 to 57 years) who were under follow-up and 55 age- and sex-matched healthy volunteers (31 males, 24 females; mean age: 38.9±8.5 years; range, 21 to 53 years) were included between March 2016 and August 2016. Toronto Alexithymia Scale (TAS), and Beck Depression Inventory (BDI) were performed to assess both patient and control groups. The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Metrology Index (BASMI), and Ankylosing Spondylitis Quality of Life (ASQoL) were performed to assess AS patients. RESULTS: The mean diagnosis time in the patient group was 5.18±4.32 (range, 1 to 18) years. Compared to the control group, depression scores were higher in the patient group and the alexithymic characteristics were significantly higher in the patient group (p<0.05). There was a positive correlation between complaint duration and BASMI, BASFI, and ASQoL scores (p<0.01). In our study, alexithymia rate was significantly higher in women (p<0.05). CONCLUSION: As in all inflammatory chronic diseases, depression and anxiety are commonly seen in AS patients. Alexithymia of these patients should be considered carefully.
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 | 0.000 |
| 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".