Bibliographic record
Abstract
Several possible remedies for migraines have been discovered to date, the most prominent ones include various antiepileptic drugs such as divalproex sodium and topiramate, as well as beta (Amiri et al., 2021)blockers such as propranolol and timolol. However many of these treatments are not entirely effective treatments for patients who face chronic migraines due to their somewhat prosaic success rates. In 1985, researchers noticed the presence of Calcitonin Gene-Related Peptide (CGRP) in the plasma increased drastically in its levels during the presence of a migraine attack (Deen et al. 2). CGRP is a neuropeptide that is involved in the dilation of both dural and cerebral blood vessels, and this interaction is believed to be the main cause of migraines. Generally, when migraines are treated with triptans (a common symptom relief medication), CGRP levels in the blood generally reduce. It was further found that certain CGRP inhibitors reduced neurogenic inflammation and lead to an increased reduction of pain during the migraine (Deen et al. 4). When faced with a multitude of treatment options for chronic migraines, one must consider inhibitors of the CGRP pathway as a possible alternative. CGRP inhibitors introduce a possible better treatment option than previous medicinal drugs. A few previously used CGRP inhibitors include erenumab, galcanezumab, and fremanezumab, which have recently been used to treat patients of chronic migraines. In this paper we will consider the various CGRP inhibitors and their advantages and disadvantages in the face of conventional chronic migraine treatment options.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".