Bibliographic record
Abstract
Objective To examine the alexithymia features of patients with depressive disorder and to investigate the associations between alexithymia and somatization. Methods 113 patients with depressive disorder and 100 healthy controls were included. The Chinese version of the 20- item Toronto Alexithymia Scale( TAS- 20),Patient Health Questionnaire( PHQ- 15),and the 17- item Hamilton Depression Scale( HAMD- 17) were applied for analysis. Results Compared with controls,patients with depressive disorder showed significantly higher scores in TAS- 20 and PHQ- 15( P 0. 05). Among the scores of TAS- 20,Factor 1( difficulty in identifying feelings) and Factor 3( externally oriented thinking) were gradually increased as the increased severity of somatization( P 0. 05). In all three factors of alexithymia,Factor 1 was strongly correlated with symptoms of pain on body( including back and arms / legs / joints) and tire( r =0. 211,r = 0. 434,P 0. 05),while Factor 3 was strongly correlated with discomfort of digestive system and head( r =0. 192,r = 0. 388,P 0. 05). According to multiple linear regression,Factor 1( β = 0. 220,95% CI 20. 55 ~ 22. 28),Factor 3( β = 0. 216,95% CI 21. 32 ~ 22. 76) and HAMD- 17 total scores( β = 0. 334,95% CI 21. 54 ~ 23. 79) were the potential risk factors of somatic complains( R2= 0. 238). Conclusion These results suggest that patients with depressive disorder have significant somatic amplifications. Alexithymia may be an affecting factor of the somatization symptoms of depression.
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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 | 0.001 | 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) | 0.001 | 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".