Investigating alexithymia in autism: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: New research suggests that, rather than representing a core feature of autism spectrum disorder (ASD), emotional processing difficulties reflect co-occurring alexithymia. Autistic individuals with alexithymia could therefore represent a specific subgroup of autism who may benefit from tailored interventions. The aim of this systematic review and meta-analysis was to explore the nature and prevalence of alexithymia in autism using the Toronto Alexithymia Scale (TAS). METHODS: Online scientific databases were searched systematically for studies on ASD popu lations using the TAS. Meta-analyses were performed to evaluate differences in scores between the ASD and neurotypical groups, and to determine the prevalence of alexithymia in these populations. RESULTS: 15 articles comparing autistic and neurotypical (NT) groups were identified. Autistic people scored significantly higher on all scores compared to the NT group. There was also a higher prevalence of alexithymia in the ASD group (49.93% compared to 4.89%), with a significantly increased risk of alexithymia in autistic participants. CONCLUSIONS: This review highlights that alexithymia is common, rather than universal, in ASD, supporting a growing body of evidence that co-occurring autism and alexithymia represents a specific subgroup in the ASD population that may have specific clinical needs. More research is needed to understand the nature and implications of co-occurring ASD and alexithymia.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".