Social cognition in Chronic Migraine with Medication Overuse: Do you mind what I think?
Bibliographic record
Abstract
Abstract Background Social cognition refers to all mental operations to decipher information needed in social interactions. Here we aimed to outline the socio-cognitive profile of Chronic Migraine with Medication Overuse (CM + MO), given they are recognized to be at risk of socio-cognitive difficulties. Given the multidimensionality of this construct, we considered: (1) socio-cognitive abilities, (2) socio-cognitive beliefs, (3) alexithymia and autism traits, and (4) social relationships. Methods Seventy-one patients suffering from CM + MO, 61 from episodic migraine (EM), and 80 healthy controls (HC) were assessed with a comprehensive battery: (1) the Faux Pas test (FP), the Strange Stories task (SS), the Reading Mind in the Eyes test (RMET), (2) the Tromsø Social Intelligence Scale, (3) the Toronto Alexithymia Scale, the Autism Spectrum Quotient, (4) the Lubben Social Network Scale, the Friendship Scale. Results CM + MO: (1) performed similar to EM but worse than HC in the FP and SS, while they were worse than EM and HC in the RMET; (2) were similar to EM and HC in social intelligence; (3) had more alexithymic/autistic traits than EM and HC; (4) reported higher levels of contact with their family members but felt little support from the people around them than HC. Conclusions CM + MO results characterized by a profile of compromised socio-cognitive abilities that affects different dimensions. These findings may have a relevant role in multiple fields related to chronic headache: from the assessment to the management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".