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Record W3210413431 · doi:10.3390/vision5040052

Aura Mapping: Where Vision and Somatosensation Meet

2021· article· en· W3210413431 on OpenAlexafffund
Frances Wilkinson

Bibliographic record

VenueVision · 2021
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsYork University
FundersMedical Research Council Canada
KeywordsAuraSomatosensory systemCortical spreading depressionMigraineMigraine with auraNeurosciencePsychologyLateralitySensory systemAudiologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

While migraine auras are most frequently visual, somatosensory auras are also relatively common. Both are characterized by the spread of activation across a cortical region containing a spatial mapping of the sensory (retinal or skin) surface. When both aura types occur within a single migraine episode, they may offer an insight into the neural mechanism which underlies them. Could they both be initiated by a single neural event, or do the timing and laterality relationships between them demand multiple triggers? The observations reported here were carried out 25 years ago by a group of six individuals with migraine with aura. They timed, described and mapped their visual and somatosensory auras as they were in progress. Twenty-nine episode reports are summarized here. The temporal relationship between the onset of the two auras was quite variable within and across participants. Various forms of the cortical spreading depression hypothesis of migraine aura are evaluated in terms of whether they can account for the timing, pattern of symptom spread and laterality of the recorded auras.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.018
GPT teacher head0.309
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes2
Has abstractyes

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