Assessing Alexithymia: the first application of TSIA on obese patients
Bibliographic record
Abstract
Background: Alexithymia is associated to physical and psychological diseases including obesity. The most widely used instrument to assess the alexithymia construct is the TAS- 20, which shows the limitations of a self-report test. To overcome these limitations, the Toronto Structured Interview for Alexithymia (TSIA) was developed as an interview-based method. The aim of the study is to assess alexithymia levels in obese patients using a multimethod measurement to evaluate both possible differences between the two instruments and their relationship with the obesity condition and psychophysical symptomatology. Methods: A total of 54 obese patients (12 women; mean BMI: 42.56±6.16), seeking surgical treatment, were enrolled in a Centre of Excellence in Bariatric Surgery in Latina. The subjects completed: TSIA, TAS-20, SCL-90-R and a sociodemographic questionnaire. Weight was measured on-site. Results: Data analysis showed a positive association between TAS-20 and TSIA (r=.289; p=.034). However, only TSIA scores were positively related to body weight (r=.393; p=.003) whereas TAS-20 was positively related to global severity index (GSI, SCL-90-R) (r=.438; p=.001). The set of linear regression models performed showed that only TSIA total score was a significant predictor of body weight (B=.944, p=.012) whereas using the TAS-20 total score a predictive effect on body weight did not emerge. Conclusions: The findings showed a different association between body weight and alexithymia according to instrument employed to evaluate alexithymia. This finding supports the importance of a multimethod assessment in some clinical conditions.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".