Alexithymia and Suicide Risk among Patients with Obsessive-compulsive Disorder
Bibliographic record
Abstract
Objective: The aim of our study was to evaluate relationships between alexithymia and suicidal ideation a sample of adult outpatients with obsessive-compulsive disorder (OCD). Methods: A sample of 86 adult outpatients with OCD (44 females and 42 males), was evaluated with a series of rating scales such as the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), the Toronto Alexithymia Scale (TAS-20), the Scale for Suicide Ideation (SSI) and Montgomery-Åsberg Depression Rating Scale (MADRS). the score of item #11 on the Y-BOCS was considered as a measure of insight. Results: Alexithymics showed a more early onset, a longer duration of illness and were more suitable to have a chronic course than nonalexithymics; they also reported higher MADRS and SSI scores. Alexithymics without insight (n=21) reported higher SSI scores than alexithymics with insight, nonalexythimics without insight and nonalexithymics with insight. A linear regression showed that chronic OCD course together with DIF dimension of TAS-20 and higher MADRS scores were significantly associated with higher suicide risk. Conclusions: Alexithymia and depressive symptoms were highly correlated in OCD patients and were significantly associated with higher suicide risk. DIF dimension of TAS-20 seems to be significantly associated with presence of suicidal ideation as well as chronic course of disorder. However, further longitudinal studies on larger samples are needed to definitely clarify this topic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".