Cephalalgia 1998;18 Suppl 22:2-7. Oslo. ISSN 0800-1952 Why is migraine so common?
Bibliographic record
Abstract
found that headache is the most common pain symptom that brings a patient to a family physician. In a telephone survey in the USA, about 93 % of people were found to have had a headache in the previous 12 months, and about two-thirds had had a headache in the previous month. In Canada (1), we were able to establish, using a telephone questionnaire with modified International Head-ache Society (illS) criteria, that of a population of 20 million about 3.2 million had migraine and about 5.8 million had tension headache. So, about 9 million people, or 40 % of the Canadian population, suffered from headache. The prevalence for migraine specifically is about 18 % for females and 5 % for males in the USA (2). In Canada, the figures were 13 % for females and 8 % for males (1). Why is migraine such a common condition? The biological nature of migraine The reason that headache is common is because it is part of the human estate, a component of the standard biological equipment. There are several strands of evidence for this. First, from the epidemiological viewpoint it is ubiquitous: studies in any part of the world, including China, Japan,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.362 | 0.134 |
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".