Prevalence of and factors associated with alexithymia among patients with chronic obstructive pulmonary disease in China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a common psychological disorder. However, few studies have investigated its prevalence and predictors in patients with chronic obstructive pulmonary disease (COPD). Therefore, we aimed to determine the prevalence and predictors of alexithymia in Chinese patients. METHODS: This cross-sectional study included 842 COPD patients to assess the prevalence and predictors of alexithymia using the 20-item Toronto Alexithymia Scale (TAS-20). We used the Hospital Anxiety and Depression Scale (HADS) to assess anxiety and depression, the modified British Medical Research Council dyspnea Rating Scale (mMRC) to assess dyspnea, St. George's Respiratory Questionnaire (SGRQ) to assess quality of life, and the age-adjusted Charlson comorbidity index (ACCI) to assess comorbidities. Alexithymia-related predictors were identified using univariate and multivariate logistic regression analyses. RESULTS: The prevalence of alexithymia in COPD patients was 23.6% (199/842). Multivariate analysis showed that age [odds ratio (OR) 0.886; 95% confidence interval (CI) 0.794-0.998], body mass index (OR 0.879; 95% CI 0.781-0.989), HADS-anxiety (OR 1.238; 95% CI 1.097-1.396), HADS-depression (OR 1.178; 95% CI 1.034-1.340), mMRC (OR 1.297; 95% CI 1.274-1.320), SGRQ (OR 1.627; 95% CI 1.401-1.890), ACCI (OR 1.165; 95% CI 1.051-1.280), and GOLD grade (OR 1.296; 95% CI 1.256-1.337) were independent predictors for alexithymia in patients with COPD. CONCLUSIONS: The prevalence of alexithymia was high in Chinese COPD patients. Anxiety, depression, dyspnea, quality of life, comorbidities, and disease severity are independent risk factors, and age and BMI are predictive factors for alexithymia in COPD patients.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".