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Record W4246807950 · doi:10.1017/s0317167100007241

The Canadian Migraine Forum

2007· article· en· W4246807950 on OpenAlexaffvenueabout
R. Allan Purdy, Werner J. Becker, Jonathan P. Gladstone, Michel Aubé S -S, Migraine Treatment, Marek Gawel, Gordon Mackie, Valerie South, Suzanne Christie, Rose Giammarco, Valerie Do, Michel Aubé

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2007
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsFoothills Medical CentreUniversity of CalgaryMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsMigraineHeadachesMedicineEpidemiologyPublic healthPsychiatryMigraine treatmentPhysical therapyFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT: The goal of the Canadian Migraine Forum was to work towards improving the lives of Canadians with migraine by reducing their migraine-related disability. This paper reviews the epidemiology and diagnosis of migraine, and the effects of migraine on health related quality of life. Many patients with migraine do not consult a physician for their headaches, and when they do they often do not receive a correct diagnosis. The discussion at the Forum concluded that better education, both for physicians and the public, on issues relating to migraine was a necessary step in improving migraine diagnosis. The degree of disability caused by migraine is often not recognized by society, and can be substantial for individuals with migraine. Once again, education of the public and of the health professionals who see these patients is key, so that the best migraine management can be instituted to minimize the impact of migraine on the individual, the family, and society at large.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.217
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.002
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.1000.010

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 head0.042
GPT teacher head0.301
Teacher spread0.259 · 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 designNot applicable
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

Citations0
Published2007
Admission routes3
Has abstractyes

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