Alexithymia in eating disorders: Systematic review and meta-analyses of studies using the Toronto Alexithymia Scale
Bibliographic record
Abstract
OBJECTIVE: The aim of this review was to synthesise the literature on the use of the Toronto Alexithymia Scale (TAS) in eating disorder populations and Healthy Controls (HCs) and to compare TAS scores in these groups. METHOD: Electronic databases were searched systematically for studies using the TAS and meta-analyses were performed to statistically compare scores on the TAS between individuals with eating disorders and HCs. RESULTS: Forty-eight studies using the TAS with both a clinical eating disorder group and HCs were identified. Of these, 44 were included in the meta-analyses, separated into: Anorexia Nervosa; Anorexia Nervosa, Restricting subtype; Anorexia Nervosa, Binge-Purge subtype, Bulimia Nervosa and Binge Eating Disorder. For all groups, there were significant differences with medium or large effect sizes between the clinical group and HCs, with the clinical group scoring significantly higher on the TAS, indicating greater difficulty with identifying and labelling emotions. CONCLUSION: Across the spectrum of eating disorders, individuals report having difficulties recognising or describing their emotions. Given the self-report design of the TAS, research to develop and evaluate treatments and clinician-administered assessments of alexithymia is warranted.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".