Bibliographic record
Abstract
Objective To study the alexithymic features of patients with depressive disorder and the correlation between alexithymia and depression.Methods Eighty-two depressive patients and eighty healthy controls were enrolled and assessed with Chinese version of the 20-item Toronto Alexithymia Scale(TAS-20)and 17-item Hamilton Depression Scale(HAMD-17).Results Compared with the controls,depressive patients showed significantly higher score in TAS-20(P0.001).In depressive patients,the scores of TAS-20,factor 1(difficulty in identifying feelings)and factor 3(externally oriented thinking)were significantly lower in highly educated individuals(P0.01,P0.05),while the scores of factor1 and factor 2(difficulty in describing feelings)were significantly lower in highly educated individuals in controls(P0.05).TAS-20 Scores in patients with severe depression were significantly higher than patients with mild to moderate depression in total score,factor 2 and factor 3(P0.01,P0.05).TAS-20 score was significantly correlated with the scores of anxiety/somatization factor,insomnia factor,blocking factor and weight losing factor of HAMD-17(r=0.231,-0.343,P0.01,P0.05).However,there was no significant correlation between cognitive factor of HAMD-17 and alexithymia factor score(P0.05).Conclusion Patients with depressive disorder have significant alexithymia.Alexithymia may be a state-dependent phenomenon in depressive patient,and correlated with 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 | 0.000 | 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) | 0.002 | 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".