Is Alexithymia a Permanent Feature in Depressed Patients?
Bibliographic record
Abstract
BACKGROUND: A six-month follow-up study was conducted to determine whether alexithymia is a permanent feature in 169 depressed outpatients. METHODS: Diagnosis of depression was confirmed by means of the Structured Clinical Interview for DSM-III-R (SCID-I). Alexithymia was screened using the 20-item version of the Toronto Alexithymia Scale (TAS-20) and severity of depression was assessed using the 21-item Beck Depression Inventory (BDI). RESULTS: Almost 40% of the patients were considered alexithymic at baseline, but only 23% at follow-up. Alexithymic patients were more often moderately or severely depressed than other patients in both study phases. The BDI scores explained 23% (at baseline) and 42% (at follow-up) of the variation in TAS-20 scores. The decrease in the TAS-20 scores was associated with a concurrent decrease in BDI scores. CONCLUSIONS: Alexithymic patients with depressive disorders do not appear to form a stable group. On the contrary, alexithymia seems to change as a function of depression. In the light of these results, alexithymia appears not to be a stable personality trait among depressed patients, and furthermore, it seems possible that alexithymic features respond to psychiatric treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".