Social cognition in Chronic Migraine with Medication Overuse: Do you mind what I think?
Bibliographic record
Abstract
This is the processed data for the manuscript “Bottiroli S, Rosi A, Sances G, Allena M, De Icco R, Lecce S, Vecchi T, Tassorelli C, Cavallini E. Social cognition in Chronic Migraine with Medication Overuse: Do you mind what I think?. To be submitted to JAMA Network Open”. Dataset refers to a study aimed to outline the socio-cognitive profile of patients with Chronic Migraine with Medication Overuse (CM+MO). Given the multidimensionality of the socio-cognitive construct, we considered: (1) socio-cognitive abilities, (2) socio-cognitive beliefs, (3) alexithymia and autism traits, and (4) social relationships. Two hundred and twelve subjects were enrolled, of which 71 suffered from CM+MO, 61 from episodic migraine (EM), and 80 were healthy controls (HC). All participants were assessed with a comprehensive socio-cognitive battery that included: (1) the Faux pas test (FP), the Strange Stories task (SS), the Reading Mind in the Eyes test (RMET), (2) the Tromso Social Intelligence Scale, (3) the Toronto Alexithymia Scale, (4) the Lubben Social Network Scale – Revised and the Friendship Scale. Data showed that CM+MO: (1) performed similar to EM but worse than HC in the FP and SS tests, while they were worse than the other two groups in the RMET; (2) were similar to EM and HC in terms of social intelligence; (3) had more alexithymic and autistic traits than EM and HC; and (4) reported higher levels of contact with their family members but felt little support from the people around them than HC. These results suggest that CM+MO patients are characterized by a profile of compromised socio-cognitive abilities that affects different dimensions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".