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Record W1978985687 · doi:10.1007/s10194-007-0320-4

Health-related quality of life among Canadians with migraine

2006· article· en· W1978985687 on OpenAlexaffabout
Paula Brna, Kevin Gordon, Joseph M. Dooley

Bibliographic record

VenueThe Journal of Headache and Pain · 2006
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsIzaak Walton Killam Health CentreCapital District Health Authority
Fundersnot available
KeywordsMigraineMedicineQuality of life (healthcare)MoodPsychiatryAnxietyMood disordersPublic healthSF-36PopulationCommunity healthMicrodata (statistics)Clinical psychologyGerontologyHealth related quality of lifeDiseaseEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

The objective was to determine the impact of migraine on health-related quality of life (HRQOL) among Canadians. Analysis was based on the public use microdata set of the Canadian Community Health Survey (CCHS), limited to those aged > or = 15 residing in Manitoba. HRQOL was measured using the SF-36 survey, which covers 8 health concepts. Multivariate linear regression was used to model each SF-36 scale against age, gender, education, income, migraine status and presence of mood or anxiety disorders. Of the 7236 CCHS respondents, 9.7% reported a diagnosis of migraine. Reported migraine predicted statistically significant (p<0.0001) lower HRQOL in all SF-36 domains with profound impairment of physical role, bodily pain and general health. Those reporting a mood disorder scored significantly lower in all domains with pronounced effects on emotional role, social functioning and general health. Reported anxiety disorder was associated with lower HRQOL in 6/8 domains. Canadians with migraine report significant impairment in HRQOL compared to the general population, independent of psychiatric morbidity.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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

Citations27
Published2006
Admission routes2
Has abstractyes

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