Characterising alexithymia in individuals with functional motor disorders: a cross-sectional analysis of the Italian Registry of Functional Motor Disorders
Bibliographic record
Abstract
BACKGROUND: Alexithymia, a personality trait characterised by difficulty in identifying and expressing emotions, may contribute to the onset and clinical presentation of functional motor disorders (FMDs), although this association remains underexplored. METHODS: From the Italian Registry of FMDs, we selected individuals recruited between November 2011 and January 2023, diagnosed with FMD according to Gupta and Lang criteria and assessed for various neurological and psychological features with validated rating scales. The main statistical analysis included regression models using the Toronto Alexithymia Scale 20 items as an explanatory variable for a set of clinical measures, adjusting for sociodemographic factors and correcting for multiple testing. RESULTS: In a cohort of 483 individuals, 20.7% had possible alexithymia and 31.5% had definite alexithymia. Higher levels of alexithymia were strongly associated with increased severity of depression (β=0.31, p<0.001), anxiety (β=0.32, p<0.001), general psychological distress (β=-0.27, p<0.001), fatigue (β=0.05, p<0.001) and pain (β=0.32, p<0.001) and moderately associated with a slower onset of FMD (β=0.02, p=0.003). Subscale analyses revealed that difficulties identifying feelings contributed most to these associations. No significant association was observed with motor symptom severity. CONCLUSIONS: Emotional processing difficulties of individuals with FMD and alexithymia might increase their vulnerability to mental health problems, pain and fatigue, possibly aggravating the overall prognosis. Further research is needed to elucidate the underlying mechanisms linking alexithymia to FMD and to explore the efficacy of interventions targeting emotional awareness and regulation in this population and to prevent long-term mental health burdens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".