Bibliographic record
Abstract
As in many other chronic conditions, adherence to prophylactic treatment in migraine is probably poor. In chronic diseases, compliance at one year does not exceed 50%. That could explain the low therapeutic gain seen with migraine preventive medications. It also renders difficult the evaluation of clinical trials on migraine prophylaxis since in most of these trials compliance is not properly assessed. From the patients' perspective, there are several factors that could explain poor adherence to recommended treatments. Essentially, these factors are the expression of the patients' subjective perception of their disease and potential remedies in a context of a positive patient-physician relationship. When migraine prophylactic treatment is considered, patients should be informed of the natural history of their disease and a diagnosis of an accelerated form of migraine should be confirmed. Prophylactic treatment at best would reduce by 50% the frequency of migraine attacks. In most studies, however, the therapeutic gain is in the order of 30-40%. Treatment should be instituted for a minimum time of two to three months and if effective maintained for 6-12 months. The outcome of prophylaxis can rarely be determined in a prospective way. The choice of prophylactic regimens remains empirical, often based on the physician's experience and perception of the mechanism of migraine. A better adherence to prophylactic treatment of migraine could possibly improve outcomes but current methods of improving adherence for chronic health problems are mostly complex and not very effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".