Is Interoception Deficit Linking Alexithymia and Eating Spectrum Symptoms? Study on a Non-Clinical Sample of Young Adults
Bibliographic record
Abstract
We investigated if interoceptive deficits could be the link between alexithymic traits and eating spectrum manifestations in a non-clinical sample. One-hundred sixty-one young adults (mean age: 23.2 ± 2.4 years) were evaluated with the Toronto Alexithymia Scale-20 (TAS-20), the Interoceptive Accuracy Scale (IAS), the Interoceptive Confusion Questionnaire (ICQ), and the Eating Attitudes Test-26 (EAT-26). Questionnaires were administered with an online procedure (Microsoft Form, Office 365 A1, Pisa, Italy) (Study Protocol #0012005/2023). We compared ICQ, IAS, and TAS-20 scores in subjects who met the threshold for a potential eating spectrum disorder according to EAT-26 scores ≥ 20 (n = 27) vs. subjects who scored <20 (n = 134), with an ANCOVA corrected for ‘age’ and ‘gender’. Subjects with EAT-26 ≥ 20, scored significantly higher at ICQ (54.4 ± 13.2 vs. 50.2 ± 6.8; p = 0.011), TAS-20 ‘Total Score’ (60.8 ± 11.9 vs. 58.1 ± 9.2; p = 0.006), and TAS-20 ‘Identifying Feelings’ (21.5 ± 7.6 vs. 17.3 ± 5.8; p = 0.0001). A binary logistic regression analysis, with EAT-26 scores < 20 vs. ≥20 as the dependent variable, and ICQ, IAS, TAS-20 total scores and dimensions, age, and gender (categorical) as covariates, showed that the only variable predicting eating spectrum symptomatology was ‘ICQ Total Score’ (OR = 1.075, 95% CI: 1.016–1.139; p = 0.013). Interoceptive confusion was the dimension linking the occurrence of alexithymic traits and eating spectrum manifestations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".