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Record W3091896502 · doi:10.1017/s0033291720003244

Alexithymia in autism: cross-sectional and longitudinal associations with social-communication difficulties, anxiety and depression symptoms

2020· article· en· W3091896502 on OpenAlexaboutno aff
Bethany Oakley, Emily J. H. Jones, Daisy Crawley, Tony Charman, Jan K. Buitelaar, Julian Tillmann, Declan Murphy, Eva Loth

Bibliographic record

VenuePsychological Medicine · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersInnovative Medicines InitiativeNational Institute for Health and Care ResearchEuropean Federation of Pharmaceutical Industries and AssociationsBrandeis UniversityEuropean CommissionAutism Speaks
KeywordsAlexithymiaAnxietyPsychologyAutismClinical psychologyToronto Alexithymia ScaleFeelingDepression (economics)Psychiatry

Abstract

fetched live from OpenAlex

Abstract Background Alexithymia (difficulties in identifying and describing emotion) is a transdiagnostic trait implicated in social–emotional and mental health problems in the general population. Many autistic individuals experience significant social-communication difficulties and elevated anxiety/depression and alexithymia. Nevertheless, the role of alexithymia in explaining individual variability in the quality/severity of social-communication difficulties and/or anxiety and depression symptoms in autism remains poorly understood. Methods In total, 337 adolescents and adults (autism N = 179) were assessed for alexithymia on the Toronto Alexithymia Scale and for social-communication difficulties, anxiety and depression symptoms. A total of 135 individuals (autism N = 76) were followed up 12–24 months later. We used regression models to establish cross-sectional and longitudinal associations between alexithymia, social-communication difficulties, anxiety and depression symptoms. Results Autistic individuals reported significantly higher alexithymia than comparison individuals ( p < 0.001, r effect size = 0.48), with 47.3% of autistic females and 21.0% of autistic males meeting cut-off for clinically relevant alexithymia (score ⩾61). Difficulties in describing feelings were particularly associated with current self-reported social-communication difficulties [ p < 0.001, β = 0.57, 95% confidence interval (CI) 0.44–0.67] and predicted later social-communication difficulties ( p = 0.02, β = 0.43, 95% CI 0.07–0.82). Difficulties in identifying feelings were particularly associated with current anxiety symptom severity ( p < 0.001, β = 0.54, 95% CI 0.41–0.77) and predicted later anxiety ( p = 0.01; β = 0.31, 95% CI 0.08–0.62). Conclusions Our findings suggest that difficulties in identifying v . describing emotion are associated with differential clinical outcomes in autism. Psychological therapies targeting emotional awareness may improve social-communication and anxiety symptoms in autism, potentially conferring long-term benefits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.080
GPT teacher head0.376
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations116
Published2020
Admission routes1
Has abstractyes

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