Sumatriptan Responsiveness and Clinical, Psychiatric and Psychologic Features in Migraine Patients
Bibliographic record
Abstract
OBJECTIVE: To compare sumatriptan responders and nonresponders in a migraine population with regard to a number of clinical, psychiatric and psychologic features. METHODS: Patients were drawn from a referral headache clinic population, and classified as responders or nonresponders. Clinical features were assessed by a written questionnaire. The lifetime prevalence of several psychiatric disorders was determined by the National Institute of Mental Health diagnostic interview schedule and personality factors were measured by the 16 Personality Factors (16PF) Questionnaire. RESULTS: Nonresponders indicated less influence on their migraine by menstrual factors, had a higher lifetime prevalence of generalized anxiety, and showed 16PF scores indicating greater shyness, self-sufficiency and perfectionism. Nonresponders were also more imaginative and less socially outgoing. CONCLUSION: Although they must be interpreted with caution due to small sample size and the multiple comparisons made, our results indicate that there may be differences between sumatriptan responders and nonresponders with regard to a number of clinical, psychiatric and psychologic factors. These results suggest that biological differences exist between the two patient groups which likely account for both the differences in their responses to sumatriptan and in the clinical features noted above.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".