Alexithymia, depression, anxiety and binge eating in obese women
Bibliographic record
Abstract
Background and Objectives: Alexithymia is a personality trait that may affect the development and course of obesity and effectiveness of treatment. The aim of the study is to assess the prevalence of alexithymia in obese women beginning a weight reduction program and determine the relationships between alexithymia and anxiety, depression, and binge eating. Methods: Obese women (n = 100; age 45 ± 13 yr) completed the following self-report inventories: Toronto Alexithymia Scale (TAS 26), Hospital Anxiety and Depression Scale (HADS), and Binge Eating Scale (BES). Results: Alexithymia was found in 46 patients and was more frequent among women who had attained only primary and vocational education than in those with a higher education level (39.1% vs. 10.9%; p = 0.002) and in those >45 years old than in younger women (30.4% vs. 69.6%; p = 0.03). The frequency of severe depression symptoms was higher in alexithymic women than in non-alexithymic women (19.6% vs. 5.6%; p = 0.03); however, the anxiety state was equally prevalent in both subgroups. The prevalence of alexithymia (52.6% vs. 44.4%) and its level (73.2 ± 8.9 vs. 71.2 ± 11.3 points) were similar in women with and without binge eating disorder. Multivariate mixed linear regression analysis revealed that higher body mass index was associated with primary and vocational education (odds ratio [OR] = 16.69) and severe depression symptoms (OR = 52.45), but not alexithymia.
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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) | 0.000 | 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) | 0.001 | 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".