Psychopathological Comorbidities and Clinical Variables in Patients With Medication Overuse Headache
Bibliographic record
Abstract
The psychopathological profile of patients with medication overuse headache (MOH) appears to be particularly complex. To better define it, we evaluated their performance on a targeted psychological profile assessment. We designed a case-control study comparing MOH patients and matched healthy controls (HC). Headache frequency, drug consumption, HIT-6, and MIDAS scores were recorded. All participants filled in the following questionnaires: Beck Depression Inventory-II Edition (BDI-2), trait subtest of State-Trait Anxiety Inventory (STAI-Y), Difficulties in Emotion Regulation Scale (DERS), Barratt Impulsiveness Scale (BIS-11), Toronto Alexithymia Scale (TAS-20). The primary endpoint was to establish if MOH patients have an altered psychopathological profile. The secondary endpoint was to establish whether the worst profile correlates with the worsening of headache and disability measures. We enrolled 48 consecutive MOH patients and 48 HC. MOH patients showed greater difficulty in recognition/regulation of emotions (DERS, TAS-20), depression (BDI-2), anxiety (STAI-Y), and impulsiveness (BIS-11). We found a positive correlation among DERS, BDI-2, STAI-Y, and BIS scores and MIDAS and HIT-6 scores and among DERS and headache frequency and drug consumption. MOH patients showed a high rate of emotion regulation difficulties, depression, and anxiety, which may negatively affect their headaches. The ability to regulate/recognize emotions may play a central role in sustaining medication overuse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".