Migraine prevalence, alexithymia, and post-traumatic stress disorder among medical students in Turkey
Bibliographic record
Abstract
The aim of this study was to investigate the prevalence of migraine, alexithymia, and post-traumatic stress disorder among medical students at Cumhuriyet University of Sivas in Turkey. A total of 250 medical students participated in this study and answered the questionnaires. The study was conducted in three stages: the self-questionnaire, the neurological evaluation, and the psychiatric evaluation. In the first stage, the subjects completed a questionnaire to assess migraine symptoms and completed the three-item Identification of Migraine Questionnaire, the Toronto Alexithymia Scale, and the Post-Traumatic Stress Disorder Checklist-Civilian Version Scale. The subjects who reported having a migraine underwent a detailed neurological evaluation conducted by a neurologist to confirm the diagnosis. In the final stage, the subjects with a migraine completed a psychiatric examination using the structured clinical interview for DSM-IV-R Axis I. The actual prevalence of migraine among these medical students was 12.6 %. The students with a migraine were diagnosed with alexithymia and post-traumatic stress disorder more frequently than those without migraine. The Migraine Disability Assessment Scale scores correlated with the post-traumatic stress disorder scores. The results of this study indicate that migraine was highly prevalent among medical students in Turkey and was associated with the alexithymic personality trait and comorbid psychiatric disorders including post-traumatic stress disorder. Treatment strategies must be developed to manage these comorbidities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".