Mentalizing self and others: A controlled study investigating the relationship between alexithymia and theory of mind in major depressive disorder
Bibliographic record
Abstract
Background: Theory of mind (ToM) and alexithymia have been reported to relate with depression in recent studies. However, data regarding the role of alexithymia and ToM in depression remain uncertain. Aim: The aim of the current study was to determine the levels of alexithymia and ToM abilities as well as their relationship with each other and clinical features in major depressive disorder (MDD). Materials and Methods: Patients diagnosed with MDD and healthy controls were undergone sociodemographic data, Beck Depression Inventory, Beck Anxiety Inventory, Toronto Alexithymia Scale (TAS-20), and reading the mind in the eyes test (RMET) to determine the depression, anxiety, alexithymia, and ToM abilities. Results: Depression, anxiety, and alexithymia levels were higher, while ToM abilities were found to be decreased in MDD patients relative to controls. A positive correlation was observed between depression levels and alexithymia levels in terms of difficulty in identifying feelings subscale and total scores of TAS-20 (P = 0.006, P = 0.036, respectively), while a positive correlation was also observed between anxiety levels and alexithymia levels in terms of difficulty in describing feelings subscale scores of TAS-20 (P = 0.02) in depressed group. No correlation was found between depression, anxiety levels, and RMET accuracy scores. Conclusion: Our results suggest alexithymia and impaired ToM abilities might be prominent but prone to be distinct clinical constructs in MDD 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".