Risk of Psychosomatic Disease Incidence According to the Dimensions of Alexithymia
Bibliographic record
Abstract
Aim and Background: A large number of studies show that alexithymia may be a risk factor for many physical and mental illnesses. This study was conducted to evaluate the predictive value of different dimensions of alexithymia for assessing vulnerability to psychosomatic diseases. Methods and Materials: This case-control survey was conducted on 146 individuals. The subjects were selected through census method from among patients referred to the Psychosomatic Clinic of Isfahan University of Medical Sciences, Iran. The participants completed the Toronto Alexithymia Scale. The obtained data were analyzed using logistic regression in SPSS software. Findings: The results showed that for every one unit increase in total score of alexithymia, the chance of psychosomatic disease incidence increased by 5% (P < 0.008). On the other hand, for every one unit increase in the subscale of difficulty in identifying feelings, the chance of psychosomatic disease incidence increases by 11%. Conclusions: This study demonstrated that alexithymia, and especially the subscale of difficulty in identifying feelings can significantly increase the risk of psychosomatic diseases. Therefore, alexithymia can be introduced as a predictive tool for psychosomatic diseases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".