Patients with substance use disorder who have higher alexithymia levels present more suicidality history: Preliminary results in an outpatient addiction treatment center in Spain
Bibliographic record
Abstract
Introduction Patients with substance use disorders (SUD) have higher alexithymia levels and present frequently suicidal ideation (SI) and suicide (SA) [1,2]. Beside, alexithymia has been related to suicidal behaviors in several psychiatric disorders[3]. Although, there are some studies on alexithymia and suicidality in SUD patients, to our knowledge there are no studies on this issue in Spanish population. Objectives To compare the alexithymia levels in SUD patients with and without SI and SA in an outpatient addiction treatment center in Spain. Methods This is a cross-sectional study performed on 110 patients (74.3%males; mean age 43.6±14.5years old) for whom we had information from the Toronto Alexithymia Scale(TAS-20) and the presence or not of lifetime SI and SA. Results Lifetime SI and SA were present in 55.5% and 35.5% of the sample respectively. The mean score of TAS-20, difficulties identifying feelings (DIF), difficulties describing feelings (DDT), and externally-oriented thinking(EOT) were 57.2±13.3, 20.0±7.0, 14.7±4.5, and 22.5±4.5 respectively. Conclusions SI and SA may be related to alexithymia levels. Hence, alexithymia should be further analyzed in SUD patients in longitudinal studies in order to analyze the bilateral association with suicidal spectrum behaviors. REFERENCES Rodríguez-Cintas L, et al. Factors associated with lifetime suicidal ideation and suicide attempts in outpatients with substance use disorders. Psychiatry Res. 2018;262:440-5. Morie KP, et al. Alexithymia and Addiction: A Review and Preliminary Data Suggesting Neurobiological Links to Reward/Loss Processing. Curr Addict Rep. 2016;3(2):239-48. Hemming L, et al. A systematic review and meta-analysis of the association between alexithymia and suicide ideation and behaviour. J Affect Disord. 2019;254:34-48.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".