2857 – Alexithymia in Patients with Schizophrenia and in Patients with Asthma
Bibliographic record
Abstract
Introduction: The pathogenesis of schizophrenia is still unclear. Genetic, biological and environmental factors are thought to intervene. It is known that schizophrenia is characterized by a difficulty to express one's feelings and identify other persons’ feelings (theory of mind) and so does alexithymia which is a psychosomatic concept. Objectives: To compared patients with schizophrenia with patients presenting a disease that is classically considered as a psycho-somatic disease: asthma. Methods: Thirty-nine patients with schizophrenia (group S) and thirty nine sex and age matched patients with asthma (group A) were assessed by the same psychiatrist using the 20 items-Toronto Alexithymia Scale validated in Arabic. Results: The mean alexithymia score in group A was 70, 45 versus 68,44 in group S, with no statistically significant difference. The prevalence of moderate alexithymia (score superior to 60) was 22, 6% in group A and 37, 5% in group S, with no statistically significant difference. The prevalence of severe alexithymia (score superior to 70) was 57% in group A and 26% in group S. Difference was statistically significant (p = 0,003). Conclusion: This study showed that severe alexithymia was significantly higher in patients with asthma compared to patients with schizophrenia. Nevertheless, the high prevalence of moderate alexithymia in patients with schizophrenia shows that alexithymia should be taken into account when treating a patient with schizophrenia, and a psychotherapy addressing this issue would be helpful.
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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 | 0.001 | 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) | 0.004 | 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".