Additional file 1 of Risk for ischemic stroke and coronary heart disease associated with migraine and migraine medication among older adults
Bibliographic record
Abstract
Additional file 1: Table S1. Definition of a history of migraine. Table S2. Definition of patient characteristics. Table S3. Antihypertensive medications, glucose lowering medications, statins, non-statin lipid-lowering medication, medications for insomnia and hormone replacement therapy. Table S4. Migraine medications. Table S5. Definitions of ischemic stroke and coronary heart disease events. Table S6. Incidence rates and hazard ratios for ischemic stroke associated with a history of migraine with and without aura among patients without a history of cardiovascular disease. Table S7. Incidence rates and hazard ratios for ischemic stroke, coronary heart disease and cardiovascular disease associated with a history of migraine and migraine medication drug classes among patients without a history of cardiovascular disease. Table S8. Incidence rates and hazard ratios for risk of ischemic stroke associated with a history of migraine without and with aura among patients with a history of cardiovascular disease. Table S9. Incidence rates and hazard ratios for ischemic stroke, coronary heart disease and cardiovascular disease associated with a history of migraine and migraine medications among patients with a history of cardiovascular disease.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.734 | 0.089 |
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".