Presumptions about the Mechanics and Causes of Headaches and Migraines over the last Century: A Historical Perspective
Bibliographic record
Abstract
For over a century now, neuroscientists have dedicated research to investigating headaches and migraines, in order to better understand this enemy common to many. Anyone who knows the misery of a migraine or severe headache understands the desperation for any new information and, most of all, a cure. Research in this area has come a long way over the years, aided in a large part by developments in research techniques and tools. Although there is still much to learn about headaches and cures are still sought with vigor, neuroscientists currently have a clearer understanding of the cause, the mechanisms, and the different forms of headaches and migraines than they did a hundred years ago. Before taking a look back at the historical perspectives of head pain that have led to today’s understanding, it is important to consider current theory of the causes and mechanisms behind headaches. A study published just last year by Watson and Drummond [1] focused on the role of cervical afferents, sensory neurons of the neck, in the pain associated with both migraines and tension-type headaches. The researchers confirmed previous claims that head pain results from deep stimulation of cervical
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".