Bibliographic record
Abstract
Migraine is a chronic neurological disorder that causes significant disability in patients and has a substantial economic impact in Canada. Effective treatment for migraine will improve our patients’ quality of life; additionally, it will reduce the economic burden generated by healthcare visits and employee absenteeism. The novel treatments in migraine target calcitonin gene-related peptide (CGRP), a neuropeptide which plays a role in the initiation of a migraine attack. Although our current understanding of migraine pathophysiology is incomplete, it is believed to involve the trigeminal nerve and its connections with the cerebral vasculature with nociceptive signals activated through a variety of neuropeptides including CGRP, substance P and nitric oxide. As a result of an improved understanding of migraine pathophysiology, the past several years have seen the advent of a variety of new therapeutic options in both the acute and prophylactic management of migraine. Although these agents represent additional options in the clinician’s arsenal, they have, in addition, introduced challenges in determining their cost-effectiveness. In this review, we provide an update on new acute and prophylactic migraine therapies and how they integrate into current practice from a primary care perspective.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".