Impact of Alexithymia on Treatment Outcome: A Naturalistic Study of Short-Term Cognitive-Behavioral Group Therapy for Panic Disorder
Bibliographic record
Abstract
BACKGROUND: It is often suggested in the literature that alexithymic patients are less responsive to psychotherapy than nonalexithymic patients. However, few empirical studies have examined this issue. Furthermore, it is unclear whether or not alexithymia itself may improve during psychotherapy. METHODS: Fifty-five consecutive outpatients with panic disorder received short-term cognitive-behavioral group therapy (CBGT) and were followed up 6 months later. Nineteen patients (35%) were on concomitant antidepressant medication. Alexithymia was measured using the 20-item Toronto Alexithymia Scale (TAS-20). Both completers and intention-to-treat analyses were calculated, taking into consideration the potentially confounding effect of comorbid conditions. RESULTS: Baseline alexithymia did not predict outcome of CBGT, neither at posttreatment nor at follow-up. The presence of comorbid axis I disorders predicted nonresponse at posttreatment but not at follow-up. TAS-20 total scores decreased over time, with the TAS-20 factors 1 (difficulty identifying feelings) and 2 (difficulty describing feelings) decreasing significantly, while factor 3 (externally oriented thinking) remained largely stable. CONCLUSIONS: These findings are encouraging for cognitive-behavioral therapists working with patients with alexithymia who suffer from panic disorder: CBGT outcome does not appear to be negatively affected by alexithymia, and some alexithymic characteristics may even be reduced following CBGT. Assessing alexithymia at treatment onset may be useful for individually tailoring therapeutic interventions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".