Perceived Scintillation Rate of Migraine Aura
Bibliographic record
Abstract
OBJECTIVE: To measure the perceived rate of flicker (temporal frequency) observed during visual auras. BACKGROUND: The flickering or scintillating quality of aura elements is a commonly described characteristic of visual migraine auras. Hypotheses about the neural mechanisms involved in aura have rarely taken this feature into account, perhaps because of a lack of quantitative data on this aspect of the aura. While a rate of 10 Hertz had been suggested in the literature, estimates have been speculative due to the difficulty of judging temporal frequencies subjectively. METHODS: Eleven participants were given portable devices that contained an adjustable light-emitting diode with which to match the flickering of their auras. Observers were asked to make flicker matches at two time points so that rate change during aura progression could be analyzed. RESULTS: Data were obtained for 36 aura episodes. The mean rate of flicker across individuals was 17.8 Hertz. Rates varied widely between individuals, but were more consistent across multiple episodes in the same observer. Rate of flicker did not appear to relate to aura side or type, or to individual characteristics such as migraine history. When episodes were analyzed for change in flicker rate over time, patterns of increase (n = 7), decrease (n = 4), and no change (n = 22) were all observed. CONCLUSIONS: When measured with an objective task, aura scintillation rates were found to be somewhat higher than previous anecdotal observations had suggested. These data are discussed in the context of two competing hypotheses concerning the neural mechanism underlying the flicker percept during migraine aura.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".