Relationship between alexithymia and depression in patients with cerebral hemorrhage
Bibliographic record
Abstract
Objective:To explore the relationship between alexithymia and depression in patients with cerebral hemorrhage(CH).Methods:All 206patients with CH were subjected to a questionnaire survey by Toronto alexithymia scale(TAS)and Hamilton depression scale(HAMD)one week after operation,and divided into the depression group and non-depression group according to the HAMD.Firstly,the general information was compared between the two groups,and then the Pearson correlation analysis and multivariate linear regression analysis were used to explore the effect of alexithymia on depression.Results:57.28%(118/206)and 41.26%(85/206)patients with CH suffered depression and alexithymia,respectively.The affective disorder of recognition,affective disorder of description,extroverted thinking and the total score of TAS in the depression group were significantly higher than those in the non-depression group(P0.05).Pearson correlation analysis revealed that the HAMD was positively correlated with TAS score,the affective disorder of recognition,affective disorder of description,extroverted thinking(r=0.389,0.374,0.281and 0.456respectively,P0.05).Multivariate linear regression analysis showed that affective disorder of recognition and extroverted thinking were the influencing factors of depression in patients with CH(P0.05).Conclusions:Alexithymia was an influencing factor of depression in patients with CH.
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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".