A Prospective Study of Alexithymia in Obsessive-Compulsive Patients Treated with Multimodal Cognitive-Behavioral Therapy
Bibliographic record
Abstract
BACKGROUND: Alexithymia as a predictor of treatment outcome in psychotherapy has often been discussed but rarely evaluated in prospective studies. The present study evaluated the absolute and relative stability of alexithymia in patients with obsessive-compulsive disorder (OCD), and the predictive value of alexithymia for the outcome of treatment. METHODS: We conducted a prospective study with 42 inpatients receiving intensive, multimodal cognitive-behavioral therapy (CBT). Patients were assessed for alexithymia at pre- and post-treatment with the 20-item Toronto Alexithymia Scale (TAS-20), for obsessive-compulsive symptoms and depression with the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and the 21-item Hamilton Depression Rating Scale (HDRS). RESULTS: OCD and comorbid depression showed a highly significant symptom-reduction from pre- to post-treatment while no absolute changes in the TAS-20 total scores and its factors 1 and 3 occurred. Only factor 2 scores decreased significantly, but with a smaller effect size than the effect sizes for the changes in Y-BOCS and HDRS. Alexithymia scores at pre-treatment correlated significantly with alexithymia scores at the end of treatment, indicating its relative stability. In the linear regression analyses, no variables were identified that predicted significantly the outcome of treatment. CONCLUSIONS: Our findings support the view that alexithymia is a stable personality trait rather than a state-dependent phenomenon in obsessive-compulsive patients. Alexithymia scores do not predict response to multimodal CBT in OCD. It might be an effect of CBT that patients could at least partly regain or newly learn the capability to describe their feelings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".