Aura phenomenon: a proposal for an etiology-based clinical classification
Bibliographic record
Abstract
BACKGROUND: The term "aura" refers to a well-defined pattern of usually positive, progressive, and reversible neurological symptoms, with spreading depolarization as the underlying mechanism. While commonly associated with migraine, aura can also occur in other neurological disorders (i.e., cerebrovascular disorders). However, current terminology inadequately describes its different underlying clinical etiologies. MAIN BODY: We propose the following terminology and etiology-based clinical classification for the aura phenomenon: (i) Migrainous Aura (when the etiology is migraine), (ii) Non-migrainous Aura (when there is an alternative etiology), (iii) Aura of uncertain clinical etiology (when etiology is unclear), and (iv) Migrainous Infarction (a typical migrainous aura in a patient with migraine with aura associated with an infarction in a corresponding anatomical brain region). CONCLUSION: This nuanced classification aims to aid in the diagnostic evaluation and phenotyping of aura phenomenon, ultimately improving the diagnosis and management of the different associated neurological conditions. Moreover, it could promote effective communication and translational mechanistic research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".