Predicting symptoms in major depression after inpatient treatment: the role of alexithymia
Bibliographic record
Abstract
Alexithymia has been considered to have a negative influence on the course of symptoms in various psychiatric disorders. Only a few studies of depressed patients have examined whether alexithymia predicts the outcome of therapeutic interventions or the course of symptoms in naturalistic settings. This prospective study investigated whether alexithymia is associated with depressive symptoms after a multimodal inpatient treatment. Forty-five inpatients suffering from acute major depression were examined in the initial phase of treatment and then again after seven weeks. Patients took part in a multimodal treatment programme comprising psychodynamic-interactional oriented individual and group therapy. The majority of patients were taking antidepressants during study participation. To assess alexithymia and depressive symptoms, the 20-item Toronto Alexithymia Scale (TAS-20), the Beck Depression Inventory II (BDI-II) and the Hamilton Depression Scale (HAMD) were administered at baseline and follow-up. When controlling for baseline depressive symptoms along with trait anxiety, high scores in the externally oriented thinking (EOT) facet of alexithymia at baseline predicted high severity of depressive symptoms at follow-up (for self-reported as well as interviewer-based scores). Inpatients suffering from major depression with a more pronounced external cognitive style might benefit less from a routine multimodal treatment approach (including psychodynamic interactional therapy, antidepressant medication, and complementary therapies). Intervention programmes might modify or account for alexithymic characteristics to improve the course of depressive symptoms in these patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".