Tratti alessitimici e consapevolezza interocettiva: implicazioni nella fenomenica dello spettro alimentare
Bibliographic record
Abstract
Background: lo spettro della condotta alimentare, in popolazioni non cliniche, può associarsi a altre variabili psicologiche. Scopo della ricerca: valutare le potenziali correlazioni tra la presenza di tratti alessitimici, la confusione/accuratezza interocettiva e la fenomenica alimentare, in un campione della popolazione generale. Materiali e metodi: lo studio è stato condotto su un database derivante da due studi approvati dal Comitato di Bioetica dell’Università di Pisa (Prot. #0012005/2023; Prot. #0017469/2024). I partecipanti, della popolazione generale sono stati valutati con un questionario socio-demografico e con Toronto-Alexithymia Scale (TAS-20), Interoceptive Accuracy Scale (IAS), Interoceptive Confusion Questionnaire (ICQ), Eating Attitudes Test-26 (EAT-26). Risultati: Il campione ha incluso 364 soggetti (età: 18-30 anni), dei quali il 68.7% (n=250) di genere femminile e il 31.3% (n=114) di genere maschile. Sono stati confrontati i punteggi ICQ, IAS e TAS-20 (ANCOVA corretta per ‘età’ e ‘genere’), nei soggetti che hanno soddisfatto la soglia per un potenziale disturbo dello spettro alimentare (punteggi EAT-26≥20; n=62) vs. soggetti con punteggio inferiore al cut-off (<20; n=302). I soggetti con EAT-26≥20 hanno ottenuto punteggi significativamente più elevati con ICQ (51.0±11.4 vs. 46.7± 8.4; p=.001) e TAS-20 ‘Total Score’ (53.1±14.9 vs. 49.9±13.1; p=.001). Di contro, hanno soddisfatto un punteggio IAS ‘Total Score’ significativamente più elevato (84.6±10.6 vs. 77.7±16.6; p=.0001). Conclusioni: la confusione interocettiva e i tratti alessitimici possono rappresentare fattori di rischio per una fenomenica di spettro alimentare nella popolazione generale. Parole chiave: alessitimia, disturbi alimentari, interocezione, anoressia nervosa, bulimia nervosa Background: the eating disorders spectrum, can be associated, in non-clinical populations, with other psychological variables. Study Aim: to evaluate the potential correlations between the presence of alexithymic features, the interoception confusion/accuracy, and eating spectrum manifestations, in a general population sample. Materials and Methods: the study was conducted on a database deriving from two studies approved by the Bioethics Committee of the University of Pisa (prot. #0012005/2023; prot. #0017469/2024). Participants were evaluated with a socio-demographic questionnaire, and with the Toronto-Alexithymia Scale (TAS-20), the Interceptive Accuracy Scale (IAS), the Interceptive Confusion Questionnaire (ICQ), and the Eating Attitudes Test-26 (Eat-26). Results: the sample included 364 subjects (age: 18-30 years; 68.7%, n=250 females, and 31.3%, n=114, males). The ICQ, IAS and TAS-20 scores were compared (ANCOVA corrected for 'age' and 'gender'), in subjects who satisfied the threshold for a potential eating spectrum disorder (EAT-26≥20; n=62) vs. subjects with a score lower than the cut-off (<20; n = 302). Subjects with EAT-26≥20 scored significantly higher on ICQ (51.0±11.4 vs. 46.7±8.4; p=.001) and TAS-20 'Total score' (53.1±14.9 vs. 49.9±13.1; p=.001). conversely, they scored significantly higher at IAS 'Total Score' (84.6±10.6 vs. 77.7±16.6; p=.0001). Conclusions: Interceptive confusion and alexithymic features can represent risk factors for eating spectrum manifestations, in the general population. Keywords: alexithymia, eating disorders, interoception, anorexia nervosa, bulimia nervosa
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".