The effect of alexithymic features on response to antidepressant medication in patients with major depression
Bibliographic record
Abstract
There has been no follow-up study regarding the effect of alexithymic features on antidepressant treatment. This study was planned to observe whether alexithymia effects short-term treatment outcome in depression. The study included 32 alexithymic and 33 nonalexithymic outpatients with major depression. Depression was assessed on the basis of the Structured Clinical Interview for DSM-IV (SCID-I). Level of depression was measured using the 17-item Hamilton Rating Scale for Depression (HAM-D). Alexithymia was screened using the Turkish version of Toronto Alexithymia Scale (TAS-20). All patients received 20 mg/d paroxetine for 10 weeks. Alexithymic and nonalexithymic patients were compared on the HAM-D scores, TAS-20 scores, and rate of response to antidepressant medication. The rate of responders, defined by a reduction of >50% from baseline in HAM-D total score, was 21.9% in the alexithymic group and 54.5% in the nonalexithymic group. Changes in the HAM-D scores were significantly correlated with the TAS-20 scores. TAS-20 scores dropped below 61 in only 31.2% of the alexithymic patients, and 68.8% of patients remained alexithymic. Whereas 50% of patients whose TAS-20 scores dropped below 61 responded to antidepressant medication, this rate was only 9.1% among patients who remained alexithymic. These findings indicated that the stability of alexithymic features had a negative effect on antidepressant treatment in 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".